Detecting and classifying copy number variation

ABSTRACT

The invention provides a method for determining copy number variations (CNV) of a sequence of interest in a test sample that comprises a mixture of nucleic acids that are known or are suspected to differ in the amount of one or more sequence of interest. The method comprises a statistical approach that accounts for accrued variability stemming from process-related, interchromosomal and inter-sequencing variability. The method is applicable to determining CNV of any fetal aneuploidy, and CNVs known or suspected to be associated with a variety of medical conditions. CNV that can be determined according to the method include trisomies and monosomies of any one or more of chromosomes 1-22, X and Y, other chromosomal polysomies, and deletions and/or duplications of segments of any one or more of the chromosomes, which can be detected by sequencing only once the nucleic acids of a test sample.

INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently with this specificationas part of the present application. Each application that the presentapplication claims benefit of or priority to as identified in theconcurrently filed Application Data Sheet is incorporated by referenceherein in its entirety and for all purposes.

BACKGROUND

One of the critical endeavors in human medical research is the discoveryof genetic abnormalities that produce adverse health consequences. Inmany cases, specific genes and/or critical diagnostic markers have beenidentified in portions of the genome that are present at abnormal copynumbers. For example, in prenatal diagnosis, extra or missing copies ofwhole chromosomes are frequently occurring genetic lesions. In cancer,deletion or multiplication of copies of whole chromosomes or chromosomalsegments, and higher level amplifications of specific regions of thegenome, are common occurrences.

Most information about copy number variation has been provided bycytogenetic resolution that has permitted recognition of structuralabnormalities. Conventional procedures for genetic screening andbiological dosimetry have utilized invasive procedures e.g.amniocentesis, to obtain cells for the analysis of karyotypes.Recognizing the need for more rapid testing methods that do not requirecell culture, fluorescence in situ hybridization (FISH), quantitativefluorescence PCR (QF-PCR) and array-Comparative Genomic Hybridization(array-CGH) have been developed as molecular-cytogenetic methods for theanalysis of copy number variations.

The advent of technologies that allow for sequencing entire genomes inrelatively short time, and the discovery of circulating cell-free DNA(cfDNA) have provided the opportunity to compare genetic materialoriginating from one chromosome to be compared to that of anotherwithout the risks associated with invasive sampling methods. However,the limitations of the existing methods, which include insufficientsensitivity stemming from the limited levels of cfDNA, and thesequencing bias of the technology stemming from the inherent nature ofgenomic information, underlie the continuing need for noninvasivemethods that would provide any or all of the specificity, sensitivity,and applicability, to reliably diagnose copy number changes in a varietyof clinical settings.

Embodiments disclosed herein fulfill some of the above needs and inparticular offers an advantage in providing a reliable method that isapplicable at least to the practice of noninvasive prenatal diagnostics,and to the diagnosis and monitoring of metastatic progression in cancerpatients.

SUMMARY

Methods are provided for determining copy number variations (CNV) of asequence of interest in a test sample that comprises a mixture ofnucleic acids that are known or are suspected to differ in the amount ofone or more sequence of interest. The method comprises a statisticalapproach that accounts for accrued variability stemming fromprocess-related, interchromosomal and inter-sequencing variability. Themethod is applicable to determining CNV of any fetal aneuploidy, andCNVs known or suspected to be associated with a variety of medicalconditions. CNV that can be determined according to the present methodinclude trisomies and monosomies of any one or more of chromosomes 1-22,X and Y, other chromosomal polysomies, and deletions and/or duplicationsof segments of any one or more of the chromosomes, which can be detectedby sequencing only once the nucleic acids of a test sample. Anyaneuploidy can be determined from sequencing information that isobtained by sequencing only once the nucleic acids of a test sample.

In one embodiment, a method is provided for determining the presence orabsence of any four or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acid molecules. The steps of the method comprise (a) obtainingsequence information for the fetal and maternal nucleic acids in thematernal test sample; (b) using the sequence information to identify anumber of sequence tags for each of any four or more chromosomes ofinterest selected from chromosomes 1-22, X and Y and to identify anumber of sequence tags for a normalizing chromosome sequence for eachof the any four or more chromosomes of interest; (c) using the number ofsequence tags identified for each of the any four or more chromosomes ofinterest and the number of sequence tags identified for each normalizingchromosome to calculate a single chromosome dose for each of the anyfour or more chromosomes of interest; and (d) comparing each of thesingle chromosome doses for each of the any four or more chromosomes ofinterest to a threshold value for each of the four or more chromosomesof interest, and thereby determining the presence or absence of any fouror more complete different fetal chromosomal aneuploidies in thematernal test sample. Step (a) can comprise sequencing at least aportion of the nucleic acid molecules of a test sample to obtain saidsequence information for the fetal and maternal nucleic acid moleculesof the test sample. In some embodiments, step (c) comprises calculatinga single chromosome dose for each of the chromosomes of interest as theratio of the number of sequence tags identified for each of thechromosomes of interest and the number of sequence tags identified forthe normalizing chromosome sequence for each of the chromosomes ofinterest. In some other embodiments, step (c) comprises (i) calculatinga sequence tag density ratio for each of the chromosomes of interest, byrelating the number of sequence tags identified for each of thechromosomes of interest in step (b) to the length of each of thechromosomes of interest; (ii) calculating a sequence tag density ratiofor each normalizing chromosome sequence by relating the number ofsequence tags identified for the sequence in step (b) to the length ofeach normalizing chromosome; and (iii) using the sequence tag densityratios calculated in steps (i) and (ii) to calculate a single chromosomedose for each of the chromosomes of interest, wherein the chromosomedose is calculated as the ratio of the sequence tag density ratio foreach of the chromosomes of interest and the sequence tag density ratiofor the normalizing chromosome sequence for each of the chromosomes ofinterest.

In another embodiment, a method is provided for determining the presenceor absence of any four or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acid molecules. The steps of the method comprise (a) obtainingsequence information for the fetal and maternal nucleic acids in thematernal test sample; (b) using the sequence information to identify anumber of sequence tags for each of any four or more chromosomes ofinterest selected from chromosomes 1-22, X and Y and to identify anumber of sequence tags for a normalizing chromosome sequence for eachof the any four or more chromosomes of interest; (c) using the number ofsequence tags identified for each of the any four or more chromosomes ofinterest and the number of sequence tags identified for each normalizingchromosome to calculate a single chromosome dose for each of the anyfour or more chromosomes of interest; and (d) comparing each of thesingle chromosome doses for each of the any four or more chromosomes ofinterest to a threshold value for each of the four or more chromosomesof interest, and thereby determining the presence or absence of any fouror more complete different fetal chromosomal aneuploidies in thematernal test sample, wherein the any four or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y comprise at leasttwenty chromosomes selected from chromosomes 1-22, X, and Y, and whereinthe presence or absence of at least twenty different complete fetalchromosomal aneuploidies is determined. Step (a) can comprise sequencingat least a portion of the nucleic acid molecules of a test sample toobtain said sequence information for the fetal and maternal nucleic acidmolecules of the test sample. In some embodiments, step (c) comprisescalculating a single chromosome dose for each of the chromosomes ofinterest as the ratio of the number of sequence tags identified for eachof the chromosomes of interest and the number of sequence tagsidentified for the normalizing chromosome sequence for each of thechromosomes of interest. In some other embodiments, step (c) comprises(i) calculating a sequence tag density ratio for each of the chromosomesof interest, by relating the number of sequence tags identified for eachof the chromosomes of interest in step (b) to the length of each of thechromosomes of interest; (ii) calculating a sequence tag density ratiofor each normalizing chromosome sequence by relating the number ofsequence tags identified for the normalizing chromosome sequence in step(b) to the length of each normalizing chromosome; and (iii) using thesequence tag density ratios calculated in steps (i) and (ii) tocalculate a single chromosome dose for each of the chromosomes ofinterest, wherein the chromosome dose is calculated as the ratio of thesequence tag density ratio for each of the chromosomes of interest andthe sequence tag density ratio for the normalizing chromosome sequencefor each of the chromosomes of interest.

In another embodiment, a method is provided for determining the presenceor absence of any four or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acid molecules. The steps of the method comprise (a) obtainingsequence information for the fetal and maternal nucleic acids in thematernal test sample; (b) using the sequence information to identify anumber of sequence tags for each of any four or more chromosomes ofinterest selected from chromosomes 1-22, X and Y and to identify anumber of sequence tags for a normalizing chromosome sequence for eachof the any four or more chromosomes of interest; (c) using the number ofsequence tags identified for each of the any four or more chromosomes ofinterest and the number of sequence tags identified for each normalizingchromosome sequence to calculate a single chromosome dose for each ofthe any four or more chromosomes of interest; and (d) comparing each ofthe single chromosome doses for each of the any four or more chromosomesof interest to a threshold value for each of the four or morechromosomes of interest, and thereby determining the presence or absenceof any four or more complete different fetal chromosomal aneuploidies inthe maternal test sample, wherein the any four or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y is all of chromosomes1-22, X, and Y, and wherein the presence or absence of complete fetalchromosomal aneuploidies of all of chromosomes 1-22, X, and Y isdetermined. Step (a) can comprise sequencing at least a portion of thenucleic acid molecules of a test sample to obtain said sequenceinformation for the fetal and maternal nucleic acid molecules of thetest sample. In some embodiments, step (c) comprises calculating asingle chromosome dose for each of the chromosomes of interest as theratio of the number of sequence tags identified for each of thechromosomes of interest and the number of sequence tags identified forthe normalizing chromosome sequence for each of the chromosomes ofinterest. In some other embodiments, step (c) comprises (i) calculatinga sequence tag density ratio for each of the chromosomes of interest, byrelating the number of sequence tags identified for each of thechromosomes of interest in step (b) to the length of each of thechromosomes of interest; (ii) calculating a sequence tag density ratiofor each normalizing chromosome sequence by relating the number ofsequence tags identified for the normalizing chromosome sequence in step(b) to the length of each normalizing chromosome; and (iii) using thesequence tag density ratios calculated in steps (i) and (ii) tocalculate a single chromosome dose for each of the chromosomes ofinterest, wherein the chromosome dose is calculated as the ratio of thesequence tag density ratio for each of the chromosomes of interest andthe sequence tag density ratio for the normalizing chromosome sequencefor each of the chromosomes of interest.

In any of the embodiments above, the normalizing chromosome sequence maybe a single chromosome selected from chromosomes 1-22, X, and Y.Alternatively, the normalizing chromosome sequence may be a group ofchromosomes selected from chromosomes 1-22, X, and Y.

In another embodiment, a method is provided for determining the presenceor absence of any one or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acids. The steps of the method comprise: (a) obtaining sequenceinformation for the fetal and maternal nucleic acids in the sample; (b)using the sequence information to identify a number of sequence tags foreach of any one or more chromosomes of interest selected fromchromosomes 1-22, X and Y and to identify a number of sequence tags fora normalizing segment sequence for each of any one or more chromosomesof interest; (c) using the number of sequence tags identified for eachof any one or more chromosomes of interest and the number of sequencetags identified for the normalizing segment sequence to calculate asingle chromosome dose for each of any one or more chromosomes ofinterest; and (d) comparing each of the single chromosome doses for eachof any one or more chromosomes of interest to a threshold value for eachof the one or more chromosomes of interest, and thereby determining thepresence or absence of one or more different complete fetal chromosomalaneuploidies in the sample. Step (a) can comprise sequencing at least aportion of the nucleic acid molecules of a test sample to obtain saidsequence information for the fetal and maternal nucleic acid moleculesof the test sample.

In some embodiments, step (c) comprises calculating a single chromosomedose for each of the chromosomes of interest as the ratio of the numberof sequence tags identified for each of the chromosomes of interest andthe number of sequence tags identified for the normalizing segmentsequence for each of the chromosomes of interest. In some otherembodiments, step (c) comprises (i) calculating a sequence tag densityratio for each of chromosomes of interest, by relating the number ofsequence tags identified for each chromosomes of interest in step (b) tothe length of each of the chromosomes of interest; (ii) calculating asequence tag density ratio for each normalizing segment sequence byrelating the number of sequence tags identified for the normalizingsegment sequence in step (b) to the length of each the normalizingchromosomes; and (iii) using the sequence tag density ratios calculatedin steps (i) and (ii) to calculate a single chromosome dose for each ofsaid chromosomes of interest, wherein said chromosome dose is calculatedas the ratio of the sequence tag density ratio for each of thechromosomes of interest and the sequence tag density ratio for thenormalizing segment sequence for each of the chromosomes of interest.

In another embodiment, a method is provided for determining the presenceor absence of any one or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acids. The steps of the method comprise: (a) obtaining sequenceinformation for the fetal and maternal nucleic acids in the sample; (b)using the sequence information to identify a number of sequence tags foreach of any one or more chromosomes of interest selected fromchromosomes 1-22, X and Y and to identify a number of sequence tags fora normalizing segment sequence for each of any one or more chromosomesof interest; (c) using the number of sequence tags identified for eachof any one or more chromosomes of interest and the number of sequencetags identified for the normalizing segment sequence to calculate asingle chromosome dose for each of any one or more chromosomes ofinterest; and (d) comparing each of the single chromosome doses for eachof any one or more chromosomes of interest to a threshold value for eachof the one or more chromosomes of interest, and thereby determining thepresence or absence of one or more different complete fetal chromosomalaneuploidies in the sample, wherein the any one or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y comprise at leasttwenty chromosomes selected from chromosomes 1-22, X, and Y, and whereinthe presence or absence of at least twenty different complete fetalchromosomal aneuploidies is determined. Step (a) can comprise sequencingat least a portion of the nucleic acid molecules of a test sample toobtain said sequence information for the fetal and maternal nucleic acidmolecules of the test sample. In some embodiments, step (c) comprisescalculating a single chromosome dose for each of the chromosomes ofinterest as the ratio of the number of sequence tags identified for eachof the chromosomes of interest and the number of sequence tagsidentified for the normalizing segment sequence for each of thechromosomes of interest. In some other embodiments, step (c) comprises(i) calculating a sequence tag density ratio for each of chromosomes ofinterest, by relating the number of sequence tags identified for eachchromosomes of interest in step (b) to the length of each of thechromosomes of interest; (ii) calculating a sequence tag density ratiofor each normalizing segment sequence by relating the number of sequencetags identified for the normalizing segment sequence in step (b) to thelength of each the normalizing chromosomes; and (iii) using the sequencetag density ratios calculated in steps (i) and (ii) to calculate asingle chromosome dose for each of said chromosomes of interest, whereinsaid chromosome dose is calculated as the ratio of the sequence tagdensity ratio for each of the chromosomes of interest and the sequencetag density ratio for the normalizing segment sequence for each of thechromosomes of interest.

In another embodiment, a method is provided for determining the presenceor absence of any one or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acids. The steps of the method comprise: (a) obtaining sequenceinformation for the fetal and maternal nucleic acids in the sample; (b)using the sequence information to identify a number of sequence tags foreach of any one or more chromosomes of interest selected fromchromosomes 1-22, X and Y and to identify a number of sequence tags fora normalizing segment sequence for each of any one or more chromosomesof interest; (c) using the number of sequence tags identified for eachof any one or more chromosomes of interest and the number of sequencetags identified for the normalizing segment sequence to calculate asingle chromosome dose for each of any one or more chromosomes ofinterest; and (d) comparing each of the single chromosome doses for eachof any one or more chromosomes of interest to a threshold value for eachof the one or more chromosomes of interest, and thereby determining thepresence or absence of one or more different complete fetal chromosomalaneuploidies in the sample, wherein the any one or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y is all of chromosomes1-22, X, and Y, and wherein the presence or absence of complete fetalchromosomal aneuploidies of all of chromosomes 1-22, X, and Y isdetermined. Step (a) can comprise sequencing at least a portion of thenucleic acid molecules of a test sample to obtain said sequenceinformation for the fetal and maternal nucleic acid molecules of thetest sample. In some embodiments, step (c) comprises calculating asingle chromosome dose for each of the chromosomes of interest as theratio of the number of sequence tags identified for each of thechromosomes of interest and the number of sequence tags identified forthe normalizing segment sequence for each of the chromosomes ofinterest. In some other embodiments, step (c) comprises (i) calculatinga sequence tag density ratio for each of chromosomes of interest, byrelating the number of sequence tags identified for each chromosomes ofinterest in step (b) to the length of each of the chromosomes ofinterest; (ii) calculating a sequence tag density ratio for eachnormalizing segment sequence by relating the number of sequence tagsidentified for the normalizing segment sequence in step (b) to thelength of each the normalizing chromosomes; and (iii) using the sequencetag density ratios calculated in steps (i) and (ii) to calculate asingle chromosome dose for each of said chromosomes of interest, whereinsaid chromosome dose is calculated as the ratio of the sequence tagdensity ratio for each of the chromosomes of interest and the sequencetag density ratio for the normalizing segment sequence for each of thechromosomes of interest.

In any one of the embodiments above, the different complete chromosomalaneuploidies are selected from complete chromosomal trisomies, completechromosomal monosomies and complete chromosomal polysomies. Thedifferent complete chromosomal aneuploidies are selected from completeaneuploidies of any one of chromosome 1-22, X, and Y. For example, thesaid different complete fetal chromosomal aneuploidies are selected fromtrisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy 21, trisomy 13,trisomy 16, trisomy 18, trisomy 22, 47,XXX, 47,XYY, and monosomy X.

In any one of the embodiments above, steps (a)-(d) are repeated for testsamples from different maternal subjects, and the method comprisesdetermining the presence or absence of any four or more differentcomplete fetal chromosomal aneuploidies in each of the test samples.

In any one of the embodiments above, the method can further comprisecalculating a normalized chromosome value (NCV), wherein the NCV relatesthe chromosome dose to the mean of the corresponding chromosome dose ina set of qualified samples as:

${NCV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thchromosome dose in a set of qualified samples, and x_(ij) is theobserved j-th chromosome dose for test sample i.

In another embodiment, a method is provided for determining the presenceor absence of different partial fetal chromosomal aneuploidies in amaternal test sample comprising fetal and maternal nucleic acids. Thesteps of the method comprise: (a) obtaining sequence information for thefetal and maternal nucleic acids in the sample; (b) using the sequenceinformation to identify a number of sequence tags for each of any one ormore segments of any one or more chromosomes of interest selected fromchromosomes 1-22, X, and Y and to identify a number of sequence tags fora normalizing segment sequence for each of any one or more segments ofany one or more chromosomes of interest; (c) using the number ofsequence tags identified for each of any one or more segments of any oneor more chromosomes of interest and said number of sequence tagsidentified for the normalizing segment sequence to calculate a singlesegment dose for each of said any one or more segments of any one ormore chromosomes of interest; and (d) comparing each of the singlesegment doses for each of any one or more segments of any one or morechromosomes of interest to a threshold value for each of any one or morechromosomal segments of any one or more chromosome of interest, andthereby determining the presence or absence of one or more differentpartial fetal chromosomal aneuploidies in the sample. Step (a) cancomprise sequencing at least a portion of the nucleic acid molecules ofa test sample to obtain said sequence information for the fetal andmaternal nucleic acid molecules of the test sample.

In some embodiments, step (c) comprises calculating a single segmentdose for each of any one or more segments of any one or more chromosomesof interest as the ratio of the number of sequence tags identified foreach of any one or more segments of any one or more chromosomes ofinterest and the number of sequence tags identified for the normalizingsegment sequence for each of the any one or more segments of any one ormore chromosomes of interest. In some other embodiments, step (c)comprises (i) calculating a sequence tag density ratio for each ofsegment of interest, by relating the number of sequence tags identifiedfor each segment of interest in step (b) to the length of each of thesegment of interest; (ii) calculating a sequence tag density ratio foreach normalizing segment sequence by relating the number of sequencetags identified for the normalizing segment sequence in step (b) to thelength of each the normalizing segment sequence; and (iii) using thesequence tag density ratios calculated in steps (i) and (ii) tocalculate a single segment dose for each segment of interest, whereinthe segment dose is calculated as the ratio of the sequence tag densityratio for each of the segments of interest and the sequence tag densityratio for the normalizing segment sequence for each of the segments ofinterest. The method can further comprise calculating a normalizedsegment value (NSV), wherein the NSV relates said segment dose to themean of the corresponding segment dose in a set of qualified samples as:

${NSV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thsegment dose in a set of qualified samples, and x_(ij) is the observedj-th segment dose for test sample i.

In embodiments of the method described whereby a chromosome dose or asegment dose is determined using a normalizing segment sequence, thenormalizing segment sequence may be a single segment of any one or moreof chromosomes 1-22, X, and Y. Alternatively, the normalizing segmentsequence may be a group of segments of any one or more of chromosomes1-22, X, and Y.

Steps (a)-(d) of the method for determining the presence or absence of apartial fetal chromosomal aneuploidy are repeated for test samples fromdifferent maternal subjects, and the method comprises determining thepresence or absence of different partial fetal chromosomal aneuploidiesin each of said samples. Partial fetal chromosomal aneuploidies that canbe determined according to the method include partial aneuploidies ofany segment of any chromosome. The partial aneuploidies can be selectedfrom partial duplications, partial multiplications, partial insertionsand partial deletions. Examples of partial aneuploidies that can bedetermined according to the method include partial monosomy ofchromosome 1, partial monosomy of chromosome 4, partial monosomy ofchromosome 5, partial monosomy of chromosome 7, partial monosomy ofchromosome 11, partial monosomy of chromosome 15, partial monosomy ofchromosome 17, partial monosomy of chromosome 18, and partial monosomyof chromosome 22.

In any one of the embodiments described above, the test sample may be amaternal sample selected from blood, plasma, serum, urine and salivasamples. In any one of the embodiments, the test sample is may be plasmasample. The nucleic acid molecules of the maternal sample are a mixtureof fetal and maternal cell-free DNA molecules. Sequencing of the nucleicacids can be performed using next generation sequencing (NGS). In someembodiments, sequencing is massively parallel sequencing usingsequencing-by-synthesis with reversible dye terminators. In otherembodiments, sequencing is sequencing-by-ligation. In yet otherembodiments, sequencing is single molecule sequencing. Optionally, anamplification step is performed prior to sequencing.

In another embodiment, a method is provided for determining the presenceor absence of any twenty or more different complete fetal chromosomalaneuploidies in a maternal plasma test sample comprising a mixture offetal and maternal cell-free DNA molecules. The steps of the methodcomprise: (a) sequencing at least a portion of the cell-free DNAmolecules to obtain sequence information for the fetal and maternalcell-free DNA molecules in the sample; (b) using the sequenceinformation to identify a number of sequence tags for each of any twentyor more chromosomes of interest selected from chromosomes 1-22, X, and Yand to identify a number of sequence tags for a normalizing chromosomefor each of said twenty or more chromosomes of interest; (c) using thenumber of sequence tags identified for each of the twenty or morechromosomes of interest and the number of sequence tags identified foreach normalizing chromosome to calculate a single chromosome dose foreach of the twenty or more chromosomes of interest; and (d) comparingeach of the single chromosome doses for each of the twenty or morechromosomes of interest to a threshold value for each of the twenty ormore chromosomes of interest, and thereby determining the presence orabsence of any twenty or more different complete fetal chromosomalaneuploidies in the sample.

In another embodiment, the invention provides a method for identifyingcopy number variation (CNV) of a sequence of interest e.g. a clinicallyrelevant sequence, in a test sample comprising the steps of: (a)obtaining a test sample and a plurality of qualified samples, said testsample comprising test nucleic acid molecules and said plurality ofqualified samples comprising qualified nucleic acid molecules; (b)obtaining sequence information for said fetal and maternal nucleic acidsin said sample; (c) based on said sequencing of said qualified nucleicacid molecules, calculating a qualified sequence dose for said qualifiedsequence of interest in each of said plurality of qualified samples,wherein said calculating a qualified sequence dose comprises determininga parameter for said qualified sequence of interest and at least onequalified normalizing sequence; (d) based on said qualified sequencedose, identifying at least one qualified normalizing sequence, whereinsaid at least one qualified normalizing sequence has the smallestvariability and/or the greatest differentiability in sequence dose insaid plurality of qualified samples; (e) based on said sequencing ofsaid nucleic acid molecules in said test sample, calculating a testsequence dose for said test sequence of interest, wherein saidcalculating a test sequence dose comprises determining a parameter forsaid test sequence of interest and at least one normalizing testsequence, and wherein said at least one normalizing test sequencecorresponds to said at least one qualified normalizing sequence; (f)comparing said test sequence dose to at least one threshold value; and(g) assessing said copy number variation of said sequence of interest insaid test sample based on the outcome of step (f). In one embodiment,the parameter for said qualified sequence of interest and at least onequalified normalizing sequence relates the number of sequence tagsmapped to said qualified sequence of interest to the number of tagsmapped to said qualified normalizing sequence, and wherein saidparameter for said test sequence of interest and at least onenormalizing test sequence relates the number of sequence tags mapped tosaid test sequence of interest to the number of tags mapped to saidnormalizing test sequence. In some embodiments, step (b) comprisessequencing at least a portion of the qualified and test nucleic acidmolecules, wherein sequencing comprises providing a plurality of mappedsequence tags for a test and a qualified sequence of interest, and forat least one test and at least one qualified normalizing sequence;sequencing at least a portion of said nucleic acid molecules of the testsample to obtain the sequence information for the fetal and maternalnucleic acid molecules of the test sample. In some embodiments, thesequencing step is performed using next generation sequencing method. Insome embodiments, the sequencing method may be a massively parallelsequencing method that uses sequencing-by-synthesis with reversible dyeterminators. In other embodiments, the sequencing method issequencing-by-ligation. In some embodiments, sequencing comprises anamplification. In other embodiments, sequencing is single moleculesequencing. The CNV of a sequence of interest is an aneuploidy, whichcan be a chromosomal or a partial aneuploidy. In some embodiments, thechromosomal aneuploidy is selected from trisomy 2, trisomy 8, trisomy 9,trisomy 20, trisomy 16, trisomy 21, trisomy 13, trisomy 18, trisomy 22,klinefelter's syndrome, 47,XXX, 47,XYY, and monosomy X. In otherembodiments, the partial aneuploidy is a partial chromosomal deletion ora partial chromosomal insertion. In some embodiments, the CNV identifiedby the method is a chromosomal or partial aneuploidy associated withcancer. In some embodiments, the test and qualified sample arebiological fluid samples e.g. plasma samples, obtained from a pregnantsubject such as a pregnant human subject. In other embodiments, a testand qualified biological fluid samples e.g. plasma samples, are obtainedfrom a subject that is known or is suspected of having cancer.

Some methods for determining the presence or absence of a fetalchromosomal aneuploidy in a maternal test sample may include thefollowing operations: (a) providing sequence reads from fetal andmaternal nucleic acids in the maternal test sample, wherein the sequencereads are provided in an electronic format; (b) aligning the sequencereads to one or more chromosome reference sequences using a computingapparatus and thereby providing sequence tags corresponding to thesequence reads; (c) computationally identifying a number of thosesequence tags that are from one or more chromosomes of interest orchromosome segments of interest and computationally identifying a numberof those sequence tags that are from at least one normalizing chromosomesequence or normalizing chromosome segment sequence for each of the oneor more chromosomes of interest or chromosome segments of interest; (d)computationally calculating, using said number of sequence tagsidentified for each of said one or more chromosomes of interest orchromosome segments of interest and said number of sequence tagsidentified for each said normalizing chromosome sequence or normalizingchromosome segment sequence, a single chromosome or segment dose foreach of said one or more chromosomes of interest or chromosome segmentsof interest; and (e) comparing, using said computing apparatus, each ofsaid single chromosome doses for each of one or more chromosomes ofinterest or chromosome segments of interest to a corresponding thresholdvalue for each of said one or more chromosomes of interest or chromosomesegments of interest, and thereby determining the presence or absence ofat least one fetal aneuploidy in said test sample. In certainimplementations, the number of sequence tags identified for each of theone or more chromosomes of interest or chromosome segments of interestis at least about 10,000, or at least about 100,000. The disclosedembodiments also provide a computer program product including anon-transitory computer readable medium on which is provided programinstructions for performing the recited operations and othercomputational operations described herein.

In some embodiments, the chromosome reference sequences have excludedregions that are present naturally in chromosomes but which do notcontribute to the number of sequence tags for any chromosome orchromosome segment. In some embodiments, a method additionally includes(i) determining whether a read under consideration aligns to a site on achromosome reference sequence where another read from the test sampleprevious aligned; and (ii) determining whether to include the read underconsideration in the number of sequence tags for a chromosome ofinterest or a chromosome segment of interest. The chromosome referencesequence may be stored on a computer readable medium.

In some embodiments, a method additionally includes sequencing at leasta portion of said nucleic acid molecules of said maternal test sample toobtain said sequence information for said fetal and maternal nucleicacid molecules of said test sample. The sequencing may involve massivelyparallel sequencing on maternal and fetal nucleic acids from thematernal test sample to produce the sequence reads.

In some embodiments, a method further includes automatically recording,using a processor, the presence or absence of a fetal chromosomalaneuploidy as determined in (d) in a patient medical record for a humansubject providing the maternal test sample. The recording may includerecording chromosome doses and/or a diagnosis based said chromosomedoses in a computer-readable medium. In some cases, the patient medicalrecord is maintained by a laboratory, physician's office, a hospital, ahealth maintenance organization, an insurance company, or a personalmedical record website. A method may further include prescribing,initiating, and/or altering treatment of a human subject from whom thematernal test sample was taken. Additionally or alternatively, themethod may include ordering and/or performing one or more additionaltests.

Some methods disclosed herein identify normalizing chromosome sequencesor normalizing chromosome segment sequences for a chromosome orchromosome segment of interest. Some such methods include the followingoperations: (a) providing a plurality of qualified samples for thechromosome or chromosome segment of interest; (b) repeatedly calculatingchromosome doses for the chromosome or chromosome segment of interestusing multiple potential normalizing chromosome sequences or normalizingchromosome segment sequences, wherein the repeated calculation isperformed with a computing apparatus; and (c) selecting a normalizingchromosome sequence or normalizing chromosome segment sequence alone orin a combination giving a smallest variability and/or a largedifferentiability in calculated doses for the chromosome or chromosomesegment of interest.

A selected normalizing chromosome sequence or normalizing chromosomesegment sequence may be part of a combination of normalizing chromosomesequences or normalizing chromosome segment sequences or it may beprovided alone, and not in combination with other normalizing chromosomesequences or normalizing chromosome segment sequences.

The disclosed embodiments provide a method for classifying a copy numbervariation in a fetal genome. The operations of the method include: (a)receiving sequence reads from fetal and maternal nucleic acids in amaternal test sample, wherein the sequence reads are provided in anelectronic format; (b) aligning the sequence reads to one or morechromosome reference sequences using a computing apparatus and therebyproviding sequence tags corresponding to the sequence reads; (c)computationally identifying a number of those sequence tags that arefrom one or more chromosomes of interest by using the computingapparatus and determining that a first chromosome of interest in thefetus harbors a copy number variation; (d) calculating a first fetalfraction value by a first method that does not use information from thetags from the first chromosome of interest; (e) calculating a secondfetal fraction value by a second method that uses information from thetags from the first chromosome; and (f) comparing the first fetalfraction value and the second fetal fraction value and using thecomparison to classify the copy number variation of the firstchromosome. In some embodiments, the method further includes sequencingcell free DNA from the maternal test sample to provide the sequencereads. In some embodiments, the method further includes obtaining thematernal test sample from a pregnant organism. In some embodiments,operation (b) includes using the computing apparatus to align at leastabout 1 million reads. In some embodiments, operation (f) can includedetermining whether the two fetal fraction values are approximatelyequal.

In some embodiments, operation (f) can further include determining thatthe two fetal fraction values are approximately equal, and therebydetermining that a ploidy assumption implicit in the second method istrue. In some embodiments, the ploidy assumption implicit in the secondmethod is that the first chromosome of interest has a completechromosomal aneuploidy. In some of these embodiments, the completechromosomal aneuploidy of the first chromosome of interest is a monosomyor a trisomy.

In some embodiments, operation (f) can include determining whether thetwo fetal fraction values are not approximately equal, and furtherinclude analyzing the tag information for the first chromosome ofinterest to determine whether (i) the first chromosome of interestharbors a partial aneuploidy, or (ii) the fetus is a mosaic.

In some embodiments, this operation can also include binning thesequence for the first chromosome of interest into a plurality ofportions; determining whether any of said portions containssignificantly more or significantly less nucleic acid than one or moreother portions; and if any of said portions contain significantly moreor significantly less nucleic acid than one or more other portions,determining that the first chromosome of interest harbors a partialaneuploidy. In one embodiment, this operation can further includedetermining that a portion of the first chromosome of interestcontaining significantly more or significantly less nucleic acid thanone or more other portions harbors the partial aneuploidy.

In one embodiments, operation (f) can also include binning the sequencefor the first chromosome of interest into a plurality of portions;determining whether any of said portions contains significantly more orsignificantly less nucleic acid than one or more other portions; and ifnone of said portions contain significantly more or significantly lessnucleic acid than one or more other portions, determining that the fetusis a mosaic.

Operation (e) can include: (a) calculating the number of sequence tagsfrom the first chromosome of interest and at least one normalizingchromosome sequence to determine a chromosome dose; and (b) calculatingthe fetal fraction value from the chromosome dose using the secondmethod. In some embodiments, this operation further includes calculatinga normalized chromosome value (NCV), wherein the second method uses thenormalized chromosome value, and wherein the NCV relates the chromosomedose to the mean of the corresponding chromosome dose in a set ofqualified samples as:

${NCV}_{iA} = \frac{R_{iA} - \overset{\_}{R_{\iota \; U}}}{\sigma_{iU}}$

where R_(lU) and σ_(iU) are the estimated mean and standard deviation,respectively, for the i-th chromosome dose in the set of qualifiedsamples, and R_(iA) is the chromosome dose calculated for the chromosomeof interest. In another embodiment, operation (d) further includes thatthe first method calculates the first fetal fraction value usinginformation from one or more polymorphisms exhibiting an allelicimbalance in the fetal and maternal nucleic acids of the maternal testsample.

In various embodiments, if the first fetal fraction value is notapproximately equal to the second fetal fraction value, the methodfurther includes (i) determining whether the copy number variationresults from a partial aneuploidy or a mosaic; and (ii) if the copynumber variation results from a partial aneuploidy, determining thelocus of the partial aneuploidy on the first chromosome of interest. Insome embodiments, determining the locus of the partial aneuploidy on thefirst chromosome of interest includes categorizing the sequence tags forthe first chromosome of interest into bins of blocks of nucleic acids inthe first chromosome of interest; and counting the mapped tags in eachbin.

Operation (e) can further include calculating the fetal fraction valueby evaluating the following expression:

ff=2×NCV _(iA) CV _(iU)

where ff is the second fetal fraction value, NCV_(iA) is the normalizedchromosome value at the i-th chromosome in an affected sample, andCV_(iU) is the coefficient of variation for doses of the chromosome ofinterest determined in the qualified samples.

In any one of the embodiments above, the first chromosome of interest isselected from a group consisting of chromosomes 1-22, X, and Y. In anyone of the above embodiments, operation (f) can classify the copy numbervariation into a classification selected from the group consisting ofcomplete chromosomal insertions, complete chromosomal deletions, partialchromosomal duplications, and partial chromosomal deletions, andmosaics.

The disclosed embodiments also provide a computer program productincluding a non-transitory computer readable medium on which is providedprogram instructions for classifying a copy number variation in a fetalgenome. The computer program product can include: (a) code for receivingsequence reads from fetal and maternal nucleic acids in a maternal testsample, wherein the sequence reads are provided in an electronic format;(b) code for aligning the sequence reads to one or more chromosomereference sequences using a computing apparatus and thereby providingsequence tags corresponding to the sequence reads; (c) code forcomputationally identifying a number of those sequence tags that arefrom one or more chromosomes of interest by using the computingapparatus and determining that a first chromosome of interest in thefetus harbors a copy number variation; (d) code for calculating a firstfetal fraction value by a first method that does not use informationfrom the tags from the first chromosome of interest; (e) code forcalculating a second fetal fraction value by a second method that usesinformation from the tags from the first chromosome; and (f) code forcomparing the first fetal fraction value and the second fetal fractionvalue and using the comparison to classify the copy number variation ofthe first chromosome. In some embodiments, the computer program productincludes code for the various operations and methods in the any of theabove embodiments of the methods disclosed.

The disclosed embodiments also provide a system for classifying a copynumber variation in a fetal genome. The system includes (a) an interfacefor receiving at least about 10,000 sequence reads from fetal andmaternal nucleic acids in a maternal test sample, wherein the sequencereads are provided in an electronic format; (b) memory for storing, atleast temporarily, a plurality of said sequence reads; (c) a processordesigned or configured with program instructions for: (i) aligning thesequence reads to one or more chromosome reference sequences and therebyproviding sequence tags corresponding to the sequence reads; (ii)identifying a number of those sequence tags that are from one or morechromosomes of interest and determining that a first chromosome ofinterest in the fetus harbors a copy number variation; (iii) calculatinga first fetal fraction value by a first method that does not useinformation from the tags from the first chromosome of interest; (iv)calculating a second fetal fraction value by a second method that usesinformation from the tags from the first chromosome; and (v) comparingthe first fetal fraction value and the second fetal fraction value andusing the comparison to classify the copy number variation of the firstchromosome. According to various embodiments, the first chromosome ofinterest is selected from a group consisting of chromosomes 1-22, X, andY. In some embodiments, the program instructions for (c)(v) includesprogram instructions for classifying the copy number variation into aclassification selected from the group consisting of completechromosomal insertions, complete chromosomal deletions, partialchromosomal duplications, and partial chromosomal deletions, andmosaics. According to various embodiments, the system can includeprogram instructions for sequencing cell free DNA from the maternal testsample to provide the sequence reads. According to some embodiments, theprogram instructions for operation (c)(i) includes program instructionsfor using the computing apparatus to align at least about 1 millionreads.

In some embodiments, the system also includes a sequencer configured tosequence the fetal and maternal nucleic acids in a maternal test sampleand provide the sequence reads in electronic format. In variousembodiments, the sequencer and the processor are located in separatefacilities and the sequencer and the processor are linked by a network.

In various embodiments, the system also further includes an apparatusfor taking the maternal test sample from a pregnant mother. According tosome embodiments, the apparatus for taking the maternal test sample andthe processor are located in separate facilities. In variousembodiments, the system also includes an apparatus for extracting cellfree DNA from the maternal test sample. In some embodiments, theapparatus for extracting cell free DNA is located in the same facilitywith the sequencer, and the apparatus for taking the maternal testsample is located in a remote facility.

According to some embodiments, the program instructions for comparingthe first fetal fraction value and the second fetal fraction value alsoinclude program instructions for determining whether the two fetalfraction values are approximately equal.

In some embodiments, the system also includes program instructions fordetermining that a ploidy assumption implicit in the second method istrue when the two fetal fraction values are approximately equal. In someembodiments, the ploidy assumption implicit in the second method is thatthe first chromosome of interest has a complete chromosomal aneuploidy.In some embodiments, the complete chromosomal aneuploidy of the firstchromosome of interest is a monosomy or a trisomy.

In some embodiments, the system also includes program instructions foranalyzing the tag information for the first chromosome of interest todetermine whether (i) first chromosome of interest harbors a partialaneuploidy, or (ii) the fetus is a mosaic, wherein the programinstructions for analyzing are configured to execute when the programinstructions for comparing the first fetal fraction value and the secondfetal fraction value indicate that the two fetal fraction values are notapproximately equal. In some embodiments, the program instructions foranalyzing the tag information for the first chromosome of interestinclude: program instructions for binning the sequence for the firstchromosome of interest into a plurality of portions; programinstructions for determining whether any of said portions containssignificantly more or significantly less nucleic acid than one or moreother portions; and program instructions for determining that the firstchromosome of interest harbors a partial aneuploidy if any of saidportions contain significantly more or significantly less nucleic acidthan one or more other portions. In some embodiments, the system furtherincludes program instructions for determining that a portion of thefirst chromosome of interest containing significantly more orsignificantly less nucleic acid than one or more other portions harborsthe partial aneuploidy.

In some embodiments, the program instructions for analyzing the taginformation for the first chromosome of interest include: programinstructions for binning the sequence for the first chromosome ofinterest into a plurality of portions; program instructions fordetermining whether any of said portions contains significantly more orsignificantly less nucleic acid than one or more other portions; andprogram instructions for determining that the fetus is a mosaic if noneof said portions contain significantly more or significantly lessnucleic acid than one or more other portions.

According to various embodiments, the system can include programinstructions for the second method of calculating the fetal fractionvalue that include: (a) program instructions for calculating the numberof sequence tags from the first chromosome of interest and at least onenormalizing chromosome sequence to determine a chromosome dose; and (b)program instructions for calculating the fetal fraction value from thechromosome dose using the second method.

In some embodiments, the system further includes program instructionsfor calculating a normalized chromosome value (NCV), wherein the programinstructions for the second method include program instructions forusing the normalized chromosome value, and wherein the programinstructions for the NCV relate the chromosome dose to the mean of thecorresponding chromosome dose in a set of qualified samples as:

${NCV}_{iA} = \frac{R_{iA} - \overset{\_}{R_{\iota \; U}}}{\sigma_{iU}}$

where R_(lU) and σ_(iU) are the estimated mean and standard deviation,respectively, for the i-th chromosome dose in the set of qualifiedsamples, and R_(iA) is the chromosome dose calculated for the chromosomeof interest. In various embodiments, the program instructions for thefirst method include program instructions for calculating the firstfetal fraction value using information from one or more polymorphismsexhibiting an allelic imbalance in the fetal and maternal nucleic acidsof the maternal test sample.

According to various embodiments, the program instructions for thesecond method of calculating the fetal fraction value include programinstructions for evaluating the following expression:

ff=2×NCV _(iA) CV _(iU)

where ff is the second fetal fraction value, NCV_(iA) is the normalizedchromosome value at the i-th chromosome in an affected sample, andCV_(iU) is the coefficient of variation for doses of the chromosome ofinterest determined in the qualified samples.

According to various embodiments, the system further includes (i)program instructions for determining whether the copy number variationresults from a partial aneuploidy or a mosaic; and (ii) programinstructions for if the copy number variation results from a partialaneuploidy, determining the locus of the partial aneuploidy on the firstchromosome of interest, wherein the program instructions in (i) and (ii)is configured to execute when the program instructions for comparing thefirst fetal fraction value and the second fetal fraction value determinethat the first fetal fraction value is not approximately equal to thesecond fetal fraction value.

In some embodiments, program instructions for determining the locus ofthe partial aneuploidy on the first chromosome of interest includeprogram instructions for categorizing the sequence tags for the firstchromosome of interest into bins of blocks of nucleic acids in the firstchromosome of interest; and program instructions for counting the mappedtags in each bin.

In certain embodiments, methods for identifying the presence of a cancerand/or an increased risk of a cancer in a mammal (e.g., a human) areprovided where the methods comprise: (a) providing sequence reads ofnucleic acids in a test sample from said mammal, wherein said testsample may comprise both genomic nucleic acids from cancerous orprecancerous cells and genomic nucleic acids from constitutive(germline) cells, wherein the sequence reads are provided in anelectronic format; (b) aligning the sequence reads to one or morechromosome reference sequences using a computing apparatus and therebyproviding sequence tags corresponding to the sequence reads; (c)computationally identifying a number of sequence tags from the fetal andmaternal nucleic acids for one or more chromosomes of interestamplification of which or deletions of which are known to be associatedwith cancers, or chromosome segments of interest amplification(s) ofwhich or deletions of which are known to be associated with cancers,wherein said chromosome or chromosome segments are selected fromchromosomes 1-22, X, and Y and segments thereof and computationallyidentifying a number of sequence tags for at least one normalizingchromosome sequence or normalizing chromosome segment sequence for eachof the one or more chromosomes of interest or chromosome segments ofinterest, wherein the number of sequence tags identified for each of theone or more chromosomes of interest or chromosome segments of interestis at least about 2,000, or at least about 5,000, or at least about10,000; (d) computationally calculating, using said number of sequencetags identified for each of said one or more chromosomes of interest orchromosome segments of interest and said number of sequence tagsidentified for each said normalizing chromosome sequence or normalizingchromosome segment sequence, a single chromosome or segment dose foreach of said one or more chromosomes of interest or chromosome segmentsof interest; and (e) comparing, using said computing apparatus, each ofsaid single chromosome doses for each of one or more chromosomes ofinterest or chromosome segments of interest to a corresponding thresholdvalue for each of said one or more chromosomes of interest or chromosomesegments of interest, and thereby determining the presence or absence ofaneuploidies in said sample, where the presence of said aneuploidiesand/or an increased number of said is an indicator of the presenceand/or increased risk of a cancer. In certain embodiments, the increasedrisk is as compared to the same subject at a different time (e.g.,earlier in time), as compared to a reference population (e.g.,optionally adjusted for gender, and/or ethnicity, and/or age, etc.), ascompared to a similar subject absent exposure to certain risk factors,and the like. In certain embodiments chromosomes of interest orchromosome segments of interest comprise whole chromosomesamplifications and/or deletions of which are known to be associated witha cancer (e.g., as described herein). In certain embodiments chromosomesof interest or chromosome segments of interest comprise chromosomesegments amplifications or deletions of which are known to be associatedwith one or more cancers. In certain embodiments the chromosome segmentscomprise substantially whole chromosome arms (e.g., as describedherein). In certain embodiments the chromosome segments comprise wholechromosome aneuploidies. In certain embodiments the whole chromosomeaneuploidies comprise a loss, while in certain other embodiments thewhole chromosome aneuploidies comprise a gain (e.g., a gain or a loss asshown in Table 1). In certain embodiments the chromosomal segments ofinterest are substantially arm-level segments comprising a p arm or a qarm of any one or more of chromosomes 1-22, X and Y. In certainembodiments the aneuploidies comprise an amplification of a substantialarm level segment of a chromosome or a deletion of a substantial armlevel segment of a chromosome. In certain embodiments the chromosomalsegments of interest substantially comprise one or more arms selectedfrom the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p,8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p,19q, 20p, 20q, 21q, and/or 22q. In certain embodiments the aneuploidiescomprise an amplification of one or more arms selected from the groupconsisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q,10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p,20q, 21q, 22q. In certain embodiments the aneuploidies comprise adeletion of one or more arms selected from the group consisting of 1p,3p, 4p, 4q, 5q, 6q, 8p, 8q, 9p, 9q, 10p, 10q, 11p, 11q, 13q, 14q, 15q,16q, 17p, 17q, 18p, 18q, 19p, 19q, 22q. In certain embodiments thechromosomal segments of interest are segments that comprise a regionand/or a gene shown in Table 3 and/or Table 5 and/or Table 4 and/orTable 6. In certain embodiments the aneuploidies comprise anamplification of a region and/or a gene shown in Table 3 and/or Table 5.In certain embodiments the aneuploidies comprise a deletion of a regionand/or a gene shown in Table 4 and/or Table 6. In certain embodimentsthe chromosomal segments of interest are segments known to contain oneor more oncogenes and/or one or more tumor suppressor genes. In certainembodiments the aneuploidies comprise an amplification of one or moreregions selected from the group consisting of 20Q13, 19q12, 1q21-1q23,8p11-p12, and the ErbB2. In certain embodiments the aneuploidiescomprise an amplification of one or more regions comprising a geneselected from the group consisting of MYC, ERBB2 (EFGR), CCND1 (CyclinD1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET, ERBBJ, CDK4,MYCB, ERBB2, AKT2, MDM2 and CDK4, and the like. In certain embodimentsthe cancer is a cancer selected from the group consisting of leukemia,ALL, brain cancer, breast cancer, colorectal cancer, dedifferentiatedliposarcoma, esophageal adenocarcinoma, esophageal squamous cell cancer,GIST, glioma, HCC, hepatocellular cancer, lung cancer, lung NSC, lungSC, medulloblastoma, melanoma, MPD, myeloproliferative disorder,cervical cancer, ovarian cancer, prostate cancer, and renal cancer. Incertain embodiments the biological sample comprise a sample selectedfrom the group consisting of whole blood, a blood fraction, saliva/oralfluid, urine, a tissue biopsy, pleural fluid, pericardial fluid,cerebral spinal fluid, and peritoneal fluid. In certain embodiments thechromosome reference sequences have excluded regions that are presentnaturally in chromosomes but that do not contribute to the number ofsequence tags for any chromosome or chromosome segment. In certainembodiments the methods further comprise determining whether a readunder consideration aligns and to a site on a chromosome referencesequence where another read previous aligned; and determining whether toinclude the read under consideration in the number of sequence tags fora chromosome of interest or a chromosome segment of interest, whereinboth determining operations are performed with the computing apparatus.In various embodiments the methods further comprise storing in acomputer readable medium (e.g., a non-transitory medium), at leasttemporarily, sequence information for said nucleic acids in said sample.In certain embodiments step (d) comprises computationally calculating asegment dose for a selected one of segments of interest as the ratio ofthe number of sequence tags identified for the selected segment ofinterest and the number of sequence tags identified for a correspondingat least one normalizing chromosome sequence or normalizing chromosomesegment sequence for the selected segment of interest. In certainembodiments the said one or more chromosome segments of interestcomprise at least 5, or at least 10, or at least 15, or at least 20, orat least 50, or at least 100 different segments of interest. In certainembodiments at least 5, or at least 10, or at least 15, or at least 20,or at least 50, or at least 100 different aneuploidies are detected. Incertain embodiments at least one normalizing chromosome sequencecomprises one or more chromosomes selected from the group consisting ofchromosomes 1-22, X, and Y. In certain embodiments said at least onenormalizing chromosome sequence comprises for each segment thechromosome corresponding to the chromosome in which said segment islocated. In certain embodiments the at least one normalizing chromosomesequence comprises for each segment the chromosome segment correspondingto the chromosome segment that is being normalized. In certainembodiments at least one normalizing chromosome sequence or normalizingchromosome segment sequence is a chromosome or segment selected for anassociated chromosome or segment of interest by (i) identifying aplurality of qualified samples for the segment of interest; (ii)repeatedly calculating chromosome doses for the selected chromosomesegment using multiple potential normalizing chromosome sequences ornormalizing chromosome segment sequences; and (iii) selecting thenormalizing chromosome segment sequence alone or in a combination givingthe smallest variability and/or greatest differentiability in calculatedchromosome doses. In certain embodiments the method further comprisescalculating a normalized segment value (NSV), wherein said NSV relatessaid segment dose to the mean of the corresponding segment dose in a setof qualified samples as described herein. In certain embodiments thenormalizing segment sequence is a single segment of any one or more ofchromosomes 1-22, X, and Y. In certain embodiments the normalizingsegment sequence is a group of segments of any one or more ofchromosomes 1-22, X, and Y. In certain embodiments the normalizingsegment comprises substantially one arm of any one or more ofchromosomes 1-22, X, and Y. In certain embodiments the method furthercomprises sequencing at least a portion of said nucleic acid moleculesof said test sample to obtain said sequence information. In certainembodiments the sequencing comprises sequencing cell free DNA from thetest sample to provide the sequence information. In certain embodimentsthe sequencing comprises sequencing cellar DNA from the test sample toprovide the sequence information. In certain embodiments the sequencingcomprises massively parallel sequencing. In certain embodiments themethod(s) further comprise automatically recording the presence orabsence of an aneuploidy as determined in (d) in a patient medicalrecord for a human subject providing the test sample, wherein therecording is performed using the processor. In certain embodiments therecording comprises recording the chromosome doses and/or a diagnosisbased said chromosome doses in a computer-readable medium. In variousembodiments the patient medical record is maintained by a laboratory,physician's office, a hospital, a health maintenance organization, aninsurance company, or a personal medical record website. In certainembodiments the determination of the presence or absence and/or numberof said aneuploidies comprises a component in a differential diagnosisfor cancer. In certain embodiments the detection of aneuploidiesindicates a positive result and said method further comprisesprescribing, initiating, and/or altering treatment of a human subjectfrom whom the test sample was taken. In certain embodiments prescribing,initiating, and/or altering treatment of a human subject from whom thetest sample was taken comprises prescribing and/or performing furtherdiagnostics to determine the presence and/or severity of a cancer. Incertain embodiments the further diagnostics comprise screening a samplefrom said subject for a biomarker of a cancer, and/or imaging saidsubject for a cancer. In certain embodiments when said method indicatesthe presence of neoplastic cells in said mammal, treating said mammal,or causing said mammal to be treated, to remove and/or to inhibit thegrowth or proliferation of said neoplastic cells. In certain embodimentstreating the mammal comprises surgically removing the neoplastic (e.g.,tumor) cells. In certain embodiments treating the mammal comprisesperforming radiotherapy or causing radiotherapy to be performed on saidmammal to kill the neoplastic cells. In certain embodiments treating themammal comprises administering or causing to be administered to saidmammal an anti-cancer drug (e.g., matuzumab, erbitux, vectibix,nimotuzumab, matuzumab, panitumumab, flourouracil, capecitabine,5-trifluoromethy 1-2′-deoxyuridine, methotrexate, raltitrexed,pemetrexed, cytosine arabinoside, 6-mercaptopurine, azathioprine,6-thioguanine, pentostatin, fludarabine, cladribine, floxuridine,cyclophosphamide, neosar, ifosfamide, thiotepa,1,3-bis(2-chloroethyl)-1-nitosourea,1,-(2-chloroethyl)-3-cyclohexyl-lnitrosourea, hexamethylmelamine,busulfan, procarbazine, dacarbazine, chlorambucil, melphalan, cisplatin,carboplatin, oxaliplatin, bendamustine, carmustine, chloromethine,dacarbazine, fotemustine, lomustine, mannosulfan, nedaplatin, nimustine,prednimustine, ranimustine, satraplatin, semustine, streptozocin,temozolomide, treosulfan, triaziquone, triethylene melamine, thiotepa,triplatin tetranitrate, trofosfamide, uramustine, doxorubicin,daunorubicin, mitoxantrone, etoposide, topotecan, teniposide,irinotecan, camptosar, camptothecin, belotecan, rubitecan, vincristine,vinblastine, vinorelbine, vindesine, paclitaxel, docetaxel, abraxane,ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imatinibmesylate, sunitinib malate, sorafenib tosylate, nilotinib hydrochloridemonohydrate/, tasigna, semaxanib, vandetanib, vatalanib, retinoic acid,a retinoic acid derivative, and the like).

In another embodiment, a computer program product for use in determiningthe presence of a cancer and/or an increased risk of a cancer in amammal is provided. The computer program product typically comprises:(a) code for providing sequence reads of nucleic acids in a test samplefrom said mammal, wherein said test sample may comprise both genomicnucleic acids from cancerous or precancerous cells and genomic nucleicacids from constitutive (germline) cells, wherein the sequence reads areprovided in an electronic format; (b) code for aligning the sequencereads to one or more chromosome reference sequences using a computingapparatus and thereby providing sequence tags corresponding to thesequence reads; (c) code for computationally identifying a number ofsequence tags from the fetal and maternal nucleic acids for one or morechromosomes of interest amplification of which or deletions of which areknown to be associated with cancers, or chromosome segments of interestamplification of which or deletions of which are known to be associatedwith cancers, wherein said chromosome or chromosome segments areselected from chromosomes 1-22, X, and Y and segments thereof andcomputationally identifying a number of sequence tags for at least onenormalizing chromosome sequence or normalizing chromosome segmentsequence for each of the one or more chromosomes of interest orchromosome segments of interest, wherein the number of sequence tagsidentified for each of the one or more chromosomes of interest orchromosome segments of interest is at least about 10,000; (d) code forcomputationally calculating, using said number of sequence tagsidentified for each of said one or more chromosomes of interest orchromosome segments of interest and said number of sequence tagsidentified for each said normalizing chromosome sequence or normalizingchromosome segment sequence, a single chromosome or segment dose foreach of said one or more chromosomes of interest or chromosome segmentsof interest; and (e) code for comparing, using said computing apparatus,each of said single chromosome doses for each of one or more chromosomesof interest or chromosome segments of interest to a correspondingthreshold value for each of said one or more chromosomes of interest orchromosome segments of interest, and thereby determining the presence orabsence of aneuploidies in said sample, where the presence of saidaneuploidies and/or an increased number of said is an indicator of thepresence and/or increased risk of a cancer. In various embodiments thecode provides instructions for performance of the diagnostic methods asdescribed above (and later herein).

Methods of treating a subject for a cancer are also provided. In certainembodiments the methods comprise performing a method for identifying thepresence of a cancer and/or an increased risk of a cancer in a mammal asdescribed herein using a sample from the subject or receiving theresults of such a method performed on the sample; and when the methodalone, or in combination with other indicator(s) from a differentialdiagnosis for a cancer indicates the presence of neoplastic cells insaid subject, treating the subject, or causing the subject to betreated, to remove and/or to inhibit the growth or proliferation of theneoplastic cells. In certain embodiments treating said subject comprisessurgically removing the cells. In certain embodiments treating thesubject comprises performing radiotherapy or causing radiotherapy to beperformed on said subject to kill said neoplastic cells. In certainembodiments treating the subject comprises administering or causing tobe administered to the subject an anti-cancer drug (e.g., matuzumab,erbitux, vectibix, nimotuzumab, matuzumab, panitumumab, flourouracil,capecitabine, 5-trifluoromethyl-2′-deoxyuridine, methotrexate,raltitrexed, pemetrexed, cytosine arabinoside, 6-mercaptopurine,azathioprine, 6-thioguanine, pentostatin, fludarabine, cladribine,floxuridine, cyclophosphamide, neosar, ifosfamide, thiotepa,1,3-bis(2-chloroethyl)-1-nitosourea,1,-(2-chloroethyl)-3-cyclohexyl-lnitrosourea, hexamethylmelamine,busulfan, procarbazine, dacarbazine, chlorambucil, melphalan, cisplatin,carboplatin, oxaliplatin, bendamustine, carmustine, chloromethine,dacarbazine, fotemustine, lomustine, mannosulfan, nedaplatin, nimustine,prednimustine, ranimustine, satraplatin, semustine, streptozocin,temozolomide, treosulfan, triaziquone, triethylene melamine, thiotepa,triplatin tetranitrate, trofosfamide, uramustine, doxorubicin,daunorubicin, mitoxantrone, etoposide, topotecan, teniposide,irinotecan, camptosar, camptothecin, belotecan, rubitecan, vincristine,vinblastine, vinorelbine, vindesine, paclitaxel, docetaxel, abraxane,ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imatinibmesylate, sunitinib malate, sorafenib tosylate, nilotinib hydrochloridemonohydrate/, tasigna, semaxanib, vandetanib, vatalanib, retinoic acid,a retinoic acid derivative, and the like).

Methods of monitoring a treatment of a subject for a cancer are alsoprovided. In various embodiments the methods comprise performing amethod for identifying the presence of a cancer and/or an increased riskof a cancer in a mammal as described herein on a sample from the subjector receiving the results of such a method performed on the sample beforeor during the treatment; and; performing the method again on a secondsample from the subject or receiving the results of such a methodperformed on the second sample at a later time during or after thetreatment; where a reduced number or severity of aneuploidy (e.g., areduced aneuploidy frequency and/or a decrease or absence of certainaneuploidies) in the second measurement (e.g., as compared to the firstmeasurement) is an indicator of a positive course of treatment and thesame or increased number or severity of aneuploidy in the secondmeasurement (e.g., as compared to the first measurement) is an indicatorof a negative course of treatment and, when said indicator is negative,adjusting said treatment regimen to a more aggressive treatment regimenand/or to a palliative treatment regimen.

Although the examples herein concern humans and the language isprimarily directed to human concerns, the concepts described herein areapplicable to genomes from any plant or animal.

INCORPORATION BY REFERENCE

All patents, patent applications, and other publications, including allsequences disclosed within these references, referred to herein areexpressly incorporated herein by reference, to the same extent as ifeach individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by reference.All documents cited are, in relevant part, incorporated herein byreference in their entireties for the purposes indicated by the contextof their citation herein. However, the citation of any document is notto be construed as an admission that it is prior art with respect to thepresent disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method 100 for determining the presence orabsence of a copy number variation in a test sample comprising a mixtureof nucleic acids.

FIG. 2 depicts workflows for preparing a sequencing library according toIllumina's full-length protocol, the abbreviated protocol (ABB), the2-STEP and 1-STEP methods as described herein. “P” represents apurification step; and “X” indicates that the purification step and orthe DNA repair are excluded.

FIG. 3 depicts a workflow of embodiments of the method for preparing asequencing library on a solid surface.

FIG. 4 illustrates a flowchart of an embodiment 400 of the method forverifying the integrity of a sample that is subjected to a multistepsingleplex sequencing bioassay.

FIG. 5 illustrates a flowchart of an embodiment 500 of the method forverifying the integrity of a plurality of samples that are subjected toa multistep multiplex sequencing bioassay.

FIG. 6 is a flowchart of a method 600 for simultaneously determining thepresence or absence of aneuploidy and the fetal fraction in a maternaltest sample comprising a mixture of fetal and maternal nucleic acids.

FIG. 7 is a flowchart of a method 700 for determining the fetal fractionin a maternal test sample comprising a mixture of fetal and maternalnucleic acids using massively parallel sequencing methods or sizeseparation of polymorphic nucleic acid sequences.

FIG. 8 is a flowchart of a method 800 for simultaneously determining thepresence or absence of fetal aneuploidy and the fetal fraction in amaternal plasma test sample enriched for polymorphic nucleic acids.

FIG. 9 is a flowchart of a method 900 for simultaneously determining thepresence or absence of fetal aneuploidy and the fetal fraction in amaternal purified cfDNA test sample that has been enriched withpolymorphic nucleic acids.

FIG. 10 is a flowchart of a method 1000 for simultaneously determiningthe presence or absence of fetal aneuploidy and the fetal fraction in asequencing library constructed from fetal and maternal nucleic acidsderived from a maternal test sample and enriched with polymorphicnucleic acids.

FIG. 11 is a flowchart outlining alternative embodiments of the methodfor determining fetal fraction by massively parallel sequencing shown inFIG. 7.

FIG. 12 is a bar diagram showing the identification of fetal andmaternal polymorphic sequences (SNPs) used to determine fetal fractionin a test sample. The total number of sequence reads (Y-axis) mapped tothe SNP sequences identified by rs numbers (X-axis), and the relativelevel of fetal nucleic acids (*) are shown.

FIG. 13 is a block diagram depicting classification of fetal andmaternal zygosity states for a given genomic position.

FIG. 14 shows a comparison of the results using a mixture model and theknown fetal fraction and estimated fetal fraction.

FIG. 15 presents error estimates by sequenced base position over 30lanes of Illumina GA2 data aligned to human genome HG18 using Eland withdefault parameters.

FIG. 16 shows that using the machine error rate as a known parameterreduces the upward bias by a point.

FIG. 17 shows that simulated data using the machine error rate as aknown parameter enhancing the case 1 and 2 error models greatly reducesthe upward bias to less than a point for fetal fraction below 0.2.

FIG. 18 is a flow chart depicting a method of classifying a CNV bycomparing fetal fraction values calculated by two different techniques.

FIG. 19 is a block diagram of a dispersed system for processing a testsample and ultimately making a diagnosis.

FIG. 20 schematically illustrates how different operations in processingtest samples may be grouped to be handled by different elements of asystem.

FIGS. 21A and 21B shows electropherograms of a cfDNA sequencing libraryprepared according to the abbreviated protocol described in Example 2a(FIG. 21A), and the protocol described in Example 2b (FIG. 21B).

FIGS. 22A-22C provide graphs showing the average (n=16) of the percentof the total number of sequence tags that mapped to each humanchromosome (% ChrN; FIG. 22A) when the sequencing library was preparedaccording to the abbreviated protocol (ABB; ⋄) and when the sequencinglibrary was prepared according to the repair-free 2-STEP method (INSOL;□); and the percent sequence tags as a function of the size of thechromosome (FIG. 22B). FIG. 22C shows the percent of the ratio of tagsmapped when libraries were prepared using the 2-STEP method to thatobtained when libraries were made using the abbreviated (ABB) method asa function of the GC content of the chromosomes.

FIGS. 23A and 23B show bar diagrams providing mean and standarddeviation of the percent of tags mapped to chromosomes X (FIG. 23A; %ChrX) and Y (FIG. 23B; % ChrY) obtained from sequencing 10 samples ofcfDNA purified from plasma of 10 pregnant women. FIG. 23A shows that agreater number of tags mapped to the X chromosome when using therepair-free method (2-STEP) relative to that obtained using theabbreviated method (ABB). FIG. 23B shows that the percent tags thatmapped to the Y chromosome when using the repair-free 2-STEP method wasnot different from that when using the abbreviated method (ABB).

FIG. 24 shows the ratio of the number of non-excluded sites (NE sites)on the reference genome (hg18) to the total number of tags mapped to thenon-excluded sites for each of 5 samples from which cfDNA was preparedand used to construct a sequencing library according to the abbreviatedprotocol (ABB) described in Example 2 (filled bars), the in solutionrepair-free protocol (2-STEP; empty bars), and the solid surfacerepair-free protocol (1-STEP; gray bars).

FIGS. 25A-25C are graphs showing the average (n=5) of the percent of thetotal number of sequence tags that mapped to each human chromosome (%ChrN; FIG. 25A) when the sequencing library was prepared on solidsurface according to the abbreviated protocol (ABB; ⋄), when thesequencing library was prepared according to the repair-free 2-STEPmethod (□), and when the library was prepared according to therepair-free 1-STEP method (Δ); and the percent sequence tags as afunction of the size of the chromosome (FIG. 25B). The regressioncoefficient for mapped tags obtained from sequencing libraries preparedaccording to the abbreviated protocol (ABB; ⋄), and the solid surfacerepair-free protocol (2-STEP; □). FIG. 25C shows the ratio of percentmapped sequence tags per chromosome obtained from sequencing librariesprepared according to the repair-free 2-STEP protocol and the tags perchromosome obtained sequencing libraries prepared according to theabbreviated protocol (ABB) as a function of the percent GC content ofeach chromosome (⋄), and the ratio of percent mapped sequence tags perchromosome obtained from sequencing libraries prepared according to therepair-free 1-STEP protocol and the tags per chromosome obtainedsequencing libraries prepared according to the abbreviated protocol(ABB) as a function of the percent GC content of each chromosome (□).

FIGS. 26A and 26B show a comparison of means and standard deviations ofthe percent of tags mapped to chromosomes X (FIG. 26A) and Y (FIG. 26B)obtained from sequencing 5 samples of cfDNA purified from plasma of 5pregnant women from the ABB, 2-STEP and 1-STEP methods. FIG. 26A showsthat a greater number of tags mapped to the X chromosome when using therepair-free methods (2-STEP and 1-STEP) relative to that obtained usingthe abbreviated method (ABB). FIG. 26B shows that the percent tags thatmapped to the Y chromosome when using the repair-free 2-STEP and 1-STEPmethods was not different from that when using the abbreviated method.

FIGS. 27A and 27B show a correlation between the amount of purifiedcfDNA used to prepare the sequencing libraries and the resulting amountof library product was made for 61 clinical samples prepared using theABB method in solution (FIG. 27A), and 35 research samples preparedusing the repair-free Solid Surface (SS) 1-STEP method (FIG. 27B).

FIG. 28 shows the correlation between the amount of cfDNA used to make alibrary and the amount of library product obtained using the 2-STEP (□),the ABB (⋄), and the 1-STEP (Δ) methods.

FIG. 29 shows the percent of indexed sequence reads that were obtainedwhen indexed libraries were prepared using the 1-STEP (open bars) andthe 2-STEP (filled bars) and sequenced as 6-plex i.e. 6 indexedsamples/flow cell lane.

FIGS. 30A and 30B are graphs showing the average (n=42) of the percentof the total number of sequence tags that mapped to each humanchromosome (% ChrN; FIG. 30A) when indexed sequencing libraries wereprepared on solid surface according to the 1-STEP method and sequencedas 6-plex; and the percent sequence tags obtained as a function of thesize of the chromosome (FIG. 30B).

FIG. 31 shows the percent sequence tags mapped to the Y chromosome(ChrY) relative to the percent tags mapped to the X chromosome (ChrX).

FIGS. 32A and 32B illustrate the distribution of the chromosome dose forchromosome 21 determined from sequencing cfDNA extracted from a set of48 blood samples obtained from human subjects each pregnant with a maleor a female fetus. Chromosome 21 doses for qualified i.e. normal forchromosome 21 (O), and trisomy 21 test samples are shown (Δ) forchromosomes 1-12 and X (FIG. 32A), and for chromosomes 1-22 and X (FIG.32B).

FIGS. 33A and 33B illustrate the distribution of the chromosome dose forchromosome 18 determined from sequencing cfDNA extracted from a set of48 blood samples obtained from human subjects each pregnant with a maleor a female fetus. Chromosome 18 doses for qualified i.e. normal forchromosome 18 (O), and trisomy 18 (Δ) test samples are shown forchromosomes 1-12 and X (FIG. 33A), and for chromosomes 1-22 and X (FIG.33B).

FIGS. 34A and 34B illustrate the distribution of the chromosome dose forchromosome 13 determined from sequencing cfDNA extracted from a set of48 blood samples obtained from human subjects each pregnant with a maleor a female fetus. Chromosome 13 doses for qualified i.e. normal forchromosome 13 (O), and trisomy 13 (Δ) test samples are shown forchromosomes 1-12 and X (FIG. 34A), and for chromosomes 1-22 and X (FIG.34B).

FIGS. 35A and 35B illustrate the distribution of the chromosome dosesfor chromosome X determined from sequencing cfDNA extracted from a setof 48 test blood samples obtained from human subjects each pregnant witha male or a female fetus. Chromosome X doses for males (46,XY; (O)),females (46,XX; (Δ)); monosomy X (45,X; (+)), and complex karyotypes(Cplx (X)) samples are shown for chromosomes 1-12 and X (FIG. 35A), andfor chromosomes 1-22 and X (FIG. 35B).

FIGS. 36A and 36B illustrate the distribution of the chromosome dosesfor chromosome Y determined from sequencing cfDNA extracted from a setof 48 test blood samples obtained from human subjects each pregnant witha male or a female fetus. Chromosome Y doses for males (46,XY; (Δ)),females (46,XX; (O)); monosomy X (45,X; (+)), and complex karyotypes(Cplx (X)) samples are shown for chromosomes 1-12 (FIG. 36A), and forchromosomes 1-22 (FIG. 36B).

FIG. 37 shows the coefficient of variation (CV) for chromosomes 21 (▪),18 (●) and 13 (Δ) that was determined from the doses shown in FIGS. 32Aand 32B, 33A and 33B, and 34A and 34B, respectively.

FIG. 38 shows the coefficient of variation (CV) for chromosomes X (▪)and Y (●) that was determined from the doses shown in FIGS. 35A and 35Band 36A and 36B, respectively.

FIG. 39 shows the cumulative distribution of GC fraction by humanchromosome. The vertical axis represents the frequency of the chromosomewith GC content below the value shown on the horizontal axis.

FIG. 40 from illustrates the sequence doses (Y-axis) for a segment ofchromosome 11 (81000082-103000103 bp) determined from sequencing cfDNAextracted from a set of 7 qualified samples (O) obtained and 1 testsample (♦) from pregnant human subjects. A sample from a subjectcarrying a fetus with a partial aneuploidy of chromosome 11 (♦) wasidentified.

FIGS. 41A-41E illustrate the distribution of normalized chromosome dosesfor chromosome 21 (41A), chromosome 18 (41B), chromosome 13 (41C),chromosome X (41D) and chromosome Y (41E) relative to the standarddeviation of the mean (Y-axis) for the corresponding chromosomes in theunaffected samples.

FIG. 42 shows normalized chromosome values for chromosomes 21 (O), 18(Δ), and 13 (□) determined in samples from training set 1 usingnormalizing chromosomes as described in Example 12.

FIG. 43 shows normalized chromosome values for chromosomes 21 (O), 18(Δ), and 13 (□) determined in samples from test set 1 using normalizingchromosomes as described in Example 12.

FIG. 44 shows normalized chromosome values for chromosomes 21 (O) and 18(Δ) determined in samples from test set 1 using the normalizing methodof Chiu et al. (normalizes the number of sequence tags identified forthe chromosome of interest with the number of sequence tags obtained forthe remaining chromosomes in the sample; see elsewhere herein Example13).

FIG. 45 shows normalized chromosome values for chromosomes 21 (O), 18(Δ), and 13 (□) determined in samples from training set 1 usingsystematically determined normalizing chromosomes (as described inExample 13).

FIGS. 46A and 46B show normalized chromosome values for chromosomes X(X-axis) and Y (Y-axis). The arrows point to the 5 (FIG. 46A) and 3(FIG. 46B) monosomy X samples that were identified in the training andtest sets, respectively, as described in Example 13.

FIG. 47 shows normalized chromosome values for chromosomes 21 (O), 18(Δ), and 13 (□) determined in samples from test set 1 usingsystematically determined normalizing chromosomes (as described inExample 13).

FIG. 48 shows normalized chromosome values for chromosome 9 (O)determined in samples from test set 1 using systematically determinednormalizing chromosomes (as described in Example 13).

FIG. 49 shows normalized chromosome values for chromosomes 1-22determined in samples from test set 1 using systematically determinednormalizing chromosomes (as described in Example 13).

FIGS. 50A and 50B are a flow diagram of the design (FIG. 50A) and adiagram for random sampling plan (FIG. 50B) for the study described inExample 16.

FIGS. 51A-51F show flow diagrams for the analyses for chromosomes 21,18, and 13 (FIGS. 51A-51C, respectively), and gender analyses forfemale, male, and monosomy X (FIGS. 51D-51F, respectively). Ovalscontain results obtained from sequencing information from thelaboratory, rectangles contain karyotype results, and rectangles withrounded corners show comparative results used to determine testperformance (sensitivity and specificity). The dashed lines in FIGS. 51Aand 51B denote the relationship between mosaic samples for T21 (n=3) andT18 (n=1) that were censored from the analysis of chromosome 21 and 18,respectively, but were correctly determined as described in Example 16.

FIG. 52 shows normalized chromosome values (NCV) versus karyotypeclassifications for chromosomes 21 (●), 18 (▪), and 13 (▴) for the testsamples of the study described in Example 16. Circled samples denoteunclassified samples with trisomy karyotype.

FIG. 53 shows normalized chromosome values for chromosome X (NCV) versuskaryotype classifications for gender classifications of the test samplesof the study described in Example 16. Samples with female karyotypes(∘), samples with male karyotypes (●), samples with 45,X (□), andsamples with other karyotypes i.e. XXX, XXY, and XYY (▪) are shown.

FIG. 54 shows a plot of normalized chromosome values for chromosome Yversus normalized chromosome values for chromosome X for the testsamples of the clinical study described in Example 16. Euploid male andfemale samples (∘), X×X samples (●), 45,X samples (X), XYY samples (▪),and X×Y samples (▴) are shown. The dashed lines show the thresholdvalues used for classifying samples as described in Example 16.

FIG. 55 schematically illustrates one embodiment of a CNV determinationmethod described herein.

FIG. 56 shows a plot from Example 17 of the percent “ff” determinedusing doses of chromosome 21 (ff₂₁) as a function of the percent “ff”determined using doses of chromosome X (ff_(X)) in a synthetic maternalsample (1) comprising DNA from a child with trisomy 21

FIG. 57 shows a plot from Example 17 of the percent “ff” determinedusing doses of chromosome 7 (ff₇) as a function of the percent “ff”determined using doses of chromosome X (ff_(X)) in a synthetic maternalsample (2) comprising DNA from a euploid mother and her child whocarries a partial deletion in chromosome 7.

FIG. 58 shows a plot from Example 17 of the percent “ff” determinedusing doses of chromosome 15 (ff₁₅) as a function of the percent “ff”determined using doses of chromosome X (ff_(X)) in a synthetic maternalsample (3) comprising DNA from a euploid mother and her child who is 25%mosaic with a partial duplication of chromosome 15.

FIG. 59 shows a plot from Example 17 of the percent “ff” determinedusing doses of chromosome 22 (ff₂₂) and the NCVs derived therefrom inartificial sample (4) comprising 0% child DNA (i), and 10% DNA from anunaffected twin son known not to have a partial chromosomal aneuploidyof chromosome 22 (ii), and 10% DNA from the affected twin son known tohave a partial chromosomal aneuploidy of chromosome 22 (iii).

FIG. 60 shows a plot from Example 18 of the CNffx versus CNff21determined in the samples comprising the fetal T21 trisomy.

FIG. 61 shows a plot from Example 18 of the CNffx versus CNff18determined in the samples comprising the fetal T18 trisomy.

FIG. 62 shows a plot from Example 18 of the CNffx versus CNff13determined in the samples comprising the fetal T13 trisomy.

FIG. 63 shows a plot from Example 19 of NCV values for chromosomes 1-22and X in the test sample.

FIG. 64 shows the fetal fraction obtained in Example 18 for the sampleswith female fetuses affected by T21.

DETAILED DESCRIPTION

The disclosed embodiments concern methods, apparatus, and systems fordetermining copy number variations (CNV) of a sequence of interest in atest sample that comprises a mixture of nucleic acids that are known orare suspected to differ in the amount of one or more sequence ofinterest. Sequences of interest include genomic segment sequencesranging from, e.g., kilobases (kb) to megabases (Mb) to entirechromosomes that are known or are suspected to be associated with agenetic or a disease condition. Examples of sequences of interestinclude chromosomes associated with well-known aneuploidies e.g. trisomy21, and segments of chromosomes that are multiplied in diseases such ascancer e.g. partial trisomy 8 in acute myeloid leukemia. CNV that can bedetermined according to the present method include monosomies andtrisomies of any one or more of autosomes 1-22, and of sex chromosomes Xand Y e.g. 45,X, 47,XXX, 47,XXY and 47,XYY, other chromosomal polysomiesi.e. tetrasomy and pentasomies including but not limited to XXXX, XXXXX,XXXXY and XYYYY, and deletions and/or duplications of segments of anyone or more of the chromosomes.

The methods employ a statistical approach that is implemented on machineprocessor(s) and accounts for accrued variability stemming from, e.g.,process-related, interchromosomal (intra-run), and inter-sequencing(inter-run) variability. The methods are applicable to determining CNVof any fetal aneuploidy, and CNVs known or suspected to be associatedwith a variety of medical conditions.

Unless otherwise indicated, the practice of the present inventioninvolves conventional techniques and apparatus commonly used inmolecular biology, microbiology, protein purification, proteinengineering, protein and DNA sequencing, and recombinant DNA fields,which are within the skill of the art. Such techniques and apparatus areknown to those of skill in the art and are described in numerous textsand reference works (See e.g., Sambrook et al., “Molecular Cloning: ALaboratory Manual”, Third Edition (Cold Spring Harbor), [2001]); andAusubel et al., “Current Protocols in Molecular Biology” [1987]).

Numeric ranges are inclusive of the numbers defining the range. It isintended that every maximum numerical limitation given throughout thisspecification includes every lower numerical limitation, as if suchlower numerical limitations were expressly written herein. Every minimumnumerical limitation given throughout this specification will includeevery higher numerical limitation, as if such higher numericallimitations were expressly written herein. Every numerical range giventhroughout this specification will include every narrower numericalrange that falls within such broader numerical range, as if suchnarrower numerical ranges were all expressly written herein.

The headings provided herein are not intended to limit the disclosure.

Unless defined otherwise herein, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art. Various scientific dictionaries that include the termsincluded herein are well known and available to those in the art.Although any methods and materials similar or equivalent to thosedescribed herein find use in the practice or testing of the embodimentsdisclosed herein, some methods and materials are described.

The terms defined immediately below are more fully described byreference to the Specification as a whole. It is to be understood thatthis disclosure is not limited to the particular methodology, protocols,and reagents described, as these may vary, depending upon the contextthey are used by those of skill in the art.

Definitions

As used herein, the singular terms “a”, “an,” and “the” include theplural reference unless the context clearly indicates otherwise. Unlessotherwise indicated, nucleic acids are written left to right in 5′ to 3′orientation and amino acid sequences are written left to right in aminotocarboxy orientation, respectively.

The term “assessing” when used herein in the context of analyzing anucleic acid sample for CNV refers to characterizing the status of achromosomal or segment aneuploidy by one of three types of calls:“normal” or “unaffected”, “affected”, and “no-call”. Thresholds forcalling normal and affected are typically set. A parameter related toaneuploidy is measured in a sample and the measured value is compared tothe thresholds. For duplication type aneuploidies, a call of affected ismade if a chromosome or segment dose (or other measured value sequencecontent) is above a defined threshold set for affected samples. For suchaneuploidies, a call of normal is made if the chromosome or segment doseis below a threshold set for normal samples. By contrast for deletiontype aneuploidies, a call of affected is made if a chromosome or segmentdose is below a defined threshold for affected samples, and a call ofnormal is made if the chromosome or segment dose is above a thresholdset for normal samples. For example, in the presence of trisomy the“normal” call is determined by the value of a parameter e.g. a testchromosome dose that is below a user-defined threshold of reliability,and the “affected” call is determined by a parameter e.g. a testchromosome dose, that is above a user-defined threshold of reliability.A “no-call” result is determined by a parameter, e.g. a test chromosomedose, that lies between the thresholds for making a “normal” or an“affected” call. The term “no-call” is used interchangeably with“unclassified”.

The term “copy number variation” herein refers to variation in thenumber of copies of a nucleic acid sequence present in a test sample incomparison with the copy number of the nucleic acid sequence present ina qualified sample. In certain embodiments, the nucleic acid sequence is1 kb or larger. In some cases, the nucleic acid sequence is a wholechromosome or significant portion thereof. A “copy number variant”refers to the sequence of nucleic acid in which copy-number differencesare found by comparison of a sequence of interest in test sample with anexpected level of the sequence of interest. For example, the level ofthe sequence of interest in the test sample is compared to that presentin a qualified sample. Copy number variants/variations includedeletions, including microdeletions, insertions, includingmicroinsertions, duplications, multiplications, inversions,translocations and complex multi-site variants. CNVs encompasschromosomal aneuploidies and partial aneuploidies.

The term “aneuploidy” herein refers to an imbalance of genetic materialcaused by a loss or gain of a whole chromosome, or part of a chromosome.

The terms “chromosomal aneuploidy” and “complete chromosomal aneuploidy”herein refer to an imbalance of genetic material caused by a loss orgain of a whole chromosome, and includes germline aneuploidy and mosaicaneuploidy.

The terms “partial aneuploidy” and “partial chromosomal aneuploidy”herein refer to an imbalance of genetic material caused by a loss orgain of part of a chromosome e.g. partial monosomy and partial trisomy,and encompasses imbalances resulting from translocations, deletions andinsertions.

The term “aneuploid sample” herein refers to a sample indicative of asubject whose chromosomal content is not euploid, i.e. the sample isindicative of a subject with an abnormal copy number of chromosomes orportions or chromosomes.

The term “aneuploid chromosome” herein refers to a chromosome that isknown or determined to be present in a sample in an abnormal copynumber.

The term “plurality” refers to more than one element. For example, theterm is used herein in reference to a number of nucleic acid moleculesor sequence tags that is sufficient to identify significant differencesin copy number variations (e.g. chromosome doses) in test samples andqualified samples using the methods disclosed herein. In someembodiments, at least about 3×106 sequence tags, at least about 5×106sequence tags, at least about 8×106 sequence tags, at least about 10×106sequence tags, at least about 15×106 sequence tags, at least about20×106 sequence tags, at least about 30×106 sequence tags, at leastabout 40×106 sequence tags, or at least about 50×106 sequence tagscomprising between about 20 and 40 bp reads are obtained for each testsample.

The terms “polynucleotide”, “nucleic acid” and “nucleic acid molecules”are used interchangeably and refer to a covalently linked sequence ofnucleotides (i.e., ribonucleotides for RNA and deoxyribonucleotides forDNA) in which the 3′ position of the pentose of one nucleotide is joinedby a phosphodiester group to the 5′ position of the pentose of the next,include sequences of any form of nucleic acid, including, but notlimited to RNA and DNA molecules such as cfDNA molecules. The term“polynucleotide” includes, without limitation, single- anddouble-stranded polynucleotide.

The term “portion” is used herein in reference to the amount of sequenceinformation of fetal and maternal nucleic acid molecules in a biologicalsample that in sum amount to less than the sequence information of 1human genome.

The term “test sample” herein refers to a sample, typically derived froma biological fluid, cell, tissue, organ, or organism, comprising anucleic acid or a mixture of nucleic acids comprising at least onenucleic acid sequence that is to be screened for copy number variation.In certain embodiments the sample comprises at least one nucleic acidsequence whose copy number is suspected of having undergone variation.Such samples include, but are not limited to sputum/oral fluid, amnioticfluid, blood, a blood fraction, or fine needle biopsy samples (e.g.,surgical biopsy, fine needle biopsy, etc.) urine, peritoneal fluid,pleural fluid, and the like. Although the sample is often taken from ahuman subject (e.g., patient), the assays can be used to copy numbervariations (CNVs) in samples from any mammal, including, but not limitedto dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample maybe used directly as obtained from the biological source or following apretreatment to modify the character of the sample. For example, suchpretreatment may include preparing plasma from blood, diluting viscousfluids and so forth. Methods of pretreatment may also involve, but arenot limited to, filtration, precipitation, dilution, distillation,mixing, centrifugation, freezing, lyophilization, concentration,amplification, nucleic acid fragmentation, inactivation of interferingcomponents, the addition of reagents, lysing, etc. If such methods ofpretreatment are employed with respect to the sample, such pretreatmentmethods are typically such that the nucleic acid(s) of interest remainin the test sample, preferably at a concentration proportional to thatin an untreated test sample (e.g., namely, a sample that is notsubjected to any such pretreatment method(s)). Such “treated” or“processed” samples are still considered to be biological “test” sampleswith respect to the methods described herein.

The term “qualified sample” herein refers to a sample comprising amixture of nucleic acids that are present in a known copy number towhich the nucleic acids in a test sample are to be compared, and it is asample that is normal i.e. not aneuploid, for the sequence of interest.In certain embodiments, qualified samples are used for identifying oneor more normalizing chromosomes or segments for a chromosome underconsideration. For example, qualified samples may be used foridentifying a normalizing chromosome for chromosome 21. In such case,the qualified sample is a sample that is not a trisomy 21 sample.Qualified samples may also be employed in determining thresholds forcalling affected samples.

The term “training set” herein refers to a set of samples that cancomprise affected and unaffected samples and are used to develop a modelfor analyzing test samples. The unaffected samples in a training set maybe used as the qualified samples to identify normalizing sequences,e.g., normalizing chromosomes, and the chromosome doses of unaffectedsamples are used to set the thresholds for each of the sequences, e.g.chromosomes, of interest. The affected samples in a training set can beused to verify that affected test samples can be easily differentiatedfrom unaffected samples.

The term “qualified nucleic acid” is used interchangeably with“qualified sequence” is a sequence against which the amount of a testsequence or test nucleic acid is compared. A qualified sequence is onepresent in a biological sample preferably at a known representation i.e.the amount of a qualified sequence is known. Generally, a qualifiedsequence is the sequence present in a “qualified sample”. A “qualifiedsequence of interest” is a qualified sequence for which the amount isknown in a qualified sample, and is a sequence that is associated with adifference in sequence representation in an individual with a medicalcondition.

The term “sequence of interest” herein refers to a nucleic acid sequencethat is associated with a difference in sequence representation inhealthy versus diseased individuals. A sequence of interest can be asequence on a chromosome that is misrepresented i.e. over- orunder-represented, in a disease or genetic condition. A sequence ofinterest may be a portion of a chromosome i.e. chromosome segment, or achromosome. For example, a sequence of interest can be a chromosome thatis over-represented in an aneuploidy condition, or a gene encoding atumor-suppressor that is under-represented in a cancer. Sequences ofinterest include sequences that are over- or under-represented in thetotal population, or a subpopulation of cells of a subject. A “qualifiedsequence of interest” is a sequence of interest in a qualified sample. A“test sequence of interest” is a sequence of interest in a test sample.

The term “normalizing sequence” herein refers to a sequence that is usedto normalize the number of sequence tags mapped to a sequence ofinterest associated with the normalizing sequence. In some embodiments,the normalizing sequence displays a variability in the number ofsequence tags that are mapped to it among samples and sequencing runsthat approximates the variability of the sequence of interest for whichit is used as a normalizing parameter, and that can differentiate anaffected sample from one or more unaffected samples. In someimplementations, the normalizing sequence best or effectivelydifferentiates, when compared to other potential normalizing sequencessuch as other chromosomes, an affected sample from one or moreunaffected samples. A “normalizing chromosome” or “normalizingchromosome sequence” is an example of a “normalizing sequence”. A“normalizing chromosome sequence” can be composed of a single chromosomeor of a group of chromosomes. A “normalizing segment” is another exampleof a “normalizing sequence”. A “normalizing segment sequence” can becomposed of a single segment of a chromosome or it can be composed oftwo or more segments of the same or of different chromosomes. In certainembodiments, a normalizing sequence is intended to normalize forvariability such as process-related, interchromosomal (intra-run), andinter-sequencing (inter-run) variability.

The term “differentiability” herein refers to the characteristic of anormalizing chromosome that enables to distinguish one or moreunaffected i.e. normal, samples from one or more affected i.e.aneuploid, samples.

The term “sequence dose” herein refers to a parameter that relates thenumber of sequence tags identified for a sequence of interest and thenumber of sequence tags identified for the normalizing sequence. In somecases, the sequence dose is the ratio of the number of sequence tagsidentified for a sequence of interest to the number of sequence tagsidentified for the normalizing sequence. In some cases, the sequencedose refers to a parameter that relates the sequence tag density of asequence of interest to the tag density of a normalizing sequence. A“test sequence dose” is a parameter that relates the sequence tagdensity of a sequence of interest, e.g. chromosome 21, to that of anormalizing sequence e.g. chromosome 9, determined in a test sample.Similarly, a “qualified sequence dose” is a parameter that relates thesequence tag density of a sequence of interest to that of a normalizingsequence determined in a qualified sample.

The term “sequence tag density” herein refers to the number of sequencereads that are mapped to a reference genome sequence; e.g. the sequencetag density for chromosome 21 is the number of sequence reads generatedby the sequencing method that are mapped to chromosome 21 of thereference genome. The term “sequence tag density ratio” herein refers tothe ratio of the number of sequence tags that are mapped to a chromosomeof the reference genome e.g. chromosome 21, to the length of thereference genome chromosome.

The term “Next Generation Sequencing (NGS)” herein refers to sequencingmethods that allow for massively parallel sequencing of clonallyamplified molecules and of single nucleic acid molecules. Non-limitingexamples of NGS include sequencing-by-synthesis using reversible dyeterminators, and sequencing-by-ligation.

The term “parameter” herein refers to a numerical value thatcharacterizes a physical property. Frequently, a parameter numericallycharacterizes a quantitative data set and/or a numerical relationshipbetween quantitative data sets. For example, a ratio (or function of aratio) between the number of sequence tags mapped to a chromosome andthe length of the chromosome to which the tags are mapped, is aparameter.

The terms “threshold value” and “qualified threshold value” herein referto any number that is used as a cutoff to characterize a sample such asa test sample containing a nucleic acid from an organism suspected ofhaving a medical condition. The threshold may be compared to a parametervalue to determine whether a sample giving rise to such parameter valuesuggests that the organism has the medical condition. In certainembodiments, a qualified threshold value is calculated using aqualifying data set and serves as a limit of diagnosis of a copy numbervariation e.g. an aneuploidy, in an organism. If a threshold is exceededby results obtained from methods disclosed herein, a subject can bediagnosed with a copy number variation e.g. trisomy 21. Appropriatethreshold values for the methods described herein can be identified byanalyzing normalizing values (e.g. chromosome doses, NCVs or NSVs)calculated for a training set of samples. Threshold values can beidentified using qualified (i.e. unaffected) samples in a training setwhich comprises both qualified (i.e. unaffected) samples and affectedsamples. The samples in the training set known to have chromosomalaneuploidies (i.e. the affected samples) can be used to confirm that thechosen thresholds are useful in differentiating affected from unaffectedsamples in a test set (see the Examples herein). The choice of athreshold is dependent on the level of confidence that the user wishesto have to make the classification. In some embodiments, the trainingset used to identify appropriate threshold values comprises at least 10,at least 20, at least 30, at least 40, at least 50, at least 60, at llet 70, at least 80, at least 90, at least 100, at least 200, at least300, at least 400, at least 500, at least 600, at least 700, at least800, at least 900, at least 1000, at least 2000, at least 3000, at least4000, or more qualified samples. It may advantageous to use larger setsof qualified samples to improve the diagnostic utility of the thresholdvalues.

The term “normalizing value” herein refers to a numerical value thatrelates the number of sequence tags identified for the sequence (e.g.chromosome or chromosome segment) of interest to the number of sequencetags identified for the normalizing sequence (e.g. normalizingchromosome or normalizing chromosome segment). For example, a“normalizing value” can be a chromosome dose as described elsewhereherein, or it can be an NCV (Normalized Chromosome Value) as describedelsewhere herein, or it can be an NSV (Normalized Segment Value) asdescribed elsewhere herein.

The term “read” refers to a sequence read from a portion of a nucleicacid sample. Typically, though not necessarily, a read represents ashort sequence of contiguous base pairs in the sample. The read may berepresented symbolically by the base pair sequence (in ATCG) of thesample portion. It may be stored in a memory device and processed asappropriate to determine whether it matches a reference sequence ormeets other criteria. A read may be obtained directly from a sequencingapparatus or indirectly from stored sequence information concerning thesample. In some cases, a read is a.DNA sequence of sufficient length(e.g., at least about 30 bp) that can be used to identify a largersequence or region, e.g. that can be aligned and specifically assignedto a chromosome or genomic region or gene.

The term “sequence tag” is herein used interchangeably with the term“mapped sequence tag” to refer to a sequence read that has beenspecifically assigned i.e. mapped, to a larger sequence e.g. a referencegenome, by alignment. Mapped sequence tags are uniquely mapped to areference genome i.e. they are assigned to a single location to thereference genome. Tags may be provided as data structures or otherassemblages of data. In certain embodiments, a tag contains a readsequence and associated information for that read such as the locationof the sequence in the genome, e.g., the position on a chromosome. Incertain embodiments, the location is specified for a positive strandorientation. A tag may be defined to provide a limit amount of mismatchin aligning to a reference genome. Tags that can be mapped to more thanone location on a reference genome i.e. tags that do not map uniquely,may not be included in the analysis.

As used herein, the terms “aligned”, “alignment”, or “aligning” refer tothe process of comparing a read or tag to a reference sequence andthereby determining whether the reference sequence contains the readsequence. If the reference sequence contains the read, the read may bemapped to the reference sequence or, in certain embodiments, to aparticular location in the reference sequence. In some cases, alignmentsimply tells whether or not a read is a member of a particular referencesequence (i.e., whether the read is present or absent in the referencesequence). For example, the alignment of a read to the referencesequence for human chromosome 13 will tell whether the read is presentin the reference sequence for chromosome 13. A tool that provides thisinformation may be called a set membership tester. In some cases, analignment additionally indicates a location in the reference sequencewhere the read or tag maps to. For example, if the reference sequence isthe whole human genome sequence, an alignment may indicate that a readis present on chromosome 13, and may further indicate that the read ison a particular strand and/or site of chromosome 13.

Aligned reads or tags are one or more sequences that are identified as amatch in terms of the order of their nucleic acid molecules to a knownsequence from a reference genome. Alignment can be done manually,although it is typically implemented by a computer algorithm, as itwould be impossible to align reads in a reasonable time period forimplementing the methods disclosed herein. One example of an algorithmfrom aligning sequences is the Efficient Local Alignment of NucleotideData (ELAND) computer program distributed as part of the IlluminaGenomics Analysis pipeline. Alternatively, a Bloom filter or similar setmembership tester may be employed to align reads to reference genomes.See U.S. Patent Application No. 61/552,374 filed Oct. 27, 2011 which isincorporated herein by reference in its entirety. The matching of asequence read in aligning can be a 100% sequence match or less than 100%(non-perfect match).

As used herein, the term “reference genome” or “reference sequence”refers to any particular known genome sequence, whether partial orcomplete, of any organism or virus which may be used to referenceidentified sequences from a subject. For example, a reference genomeused for human subjects as well as many other organisms is found at theNational Center for Biotechnology Information at www.ncbi.nlm.nih.gov. A“genome” refers to the complete genetic information of an organism orvirus, expressed in nucleic acid sequences.

In various embodiments, the reference sequence is significantly largerthan the reads that are aligned to it. For example, it may be at leastabout 100 times larger, or at least about 1000 times larger, or at leastabout 10,000 times larger, or at least about 10⁵ times larger, or atleast about 10⁶ times larger, or at least about 10⁷ times larger.

In one example, the reference sequence is that of a full length humangenome. Such sequences may be referred to as genomic referencesequences. In another example, the reference sequence is limited to aspecific human chromosome such as chromosome 13. Such sequences may bereferred to as chromosome reference sequences. Other examples ofreference sequences include genomes of other species, as well aschromosomes, sub-chromosomal regions (such as strands), etc. of anyspecies.

In various embodiments, the reference sequence is a consensus sequenceor other combination derived from multiple individuals. However, incertain applications, the reference sequence may be taken from aparticular individual.

The term “artificial target sequences genome” herein refers to agrouping of known sequences that encompass alleles of known polymorphicsites. For example, a “SNP reference genome” is an artificial targetsequences genome comprising a grouping of sequences that encompassalleles of known SNPs.

The term “clinically-relevant sequence” herein refers to a nucleic acidsequence that is known or is suspected to be associated or implicatedwith a genetic or disease condition. Determining the absence or presenceof a clinically-relevant sequence can be useful in determining adiagnosis or confirming a diagnosis of a medical condition, or providinga prognosis for the development of a disease.

The term “derived” when used in the context of a nucleic acid or amixture of nucleic acids, herein refers to the means whereby the nucleicacid(s) are obtained from the source from which they originate. Forexample, in one embodiment, a mixture of nucleic acids that is derivedfrom two different genomes means that the nucleic acids e.g. cfDNA, werenaturally released by cells through naturally occurring processes suchas necrosis or apoptosis. In another embodiment, a mixture of nucleicacids that is derived from two different genomes means that the nucleicacids were extracted from two different types of cells from a subject.

The term “patient sample” herein refers to a biological sample obtainedfrom a patient i.e. a recipient of medical attention, care or treatment.The patient sample can be any of the samples described herein. Incertain embodiments, the patient sample is obtained by non-invasiveprocedures e.g. peripheral blood sample or a stool sample. The methodsdescribed herein need not be limited to humans. Thus, various veterinaryapplications are contemplated in which case the patient sample may be asample from a non-human mammal (e.g., a feline, a porcine, an equine, abovine, and the like).

The term “mixed sample” herein refers to a sample containing a mixtureof nucleic acids, which are derived from different genomes.

The term “maternal sample” herein refers to a biological sample obtainedfrom a pregnant subject e.g. a woman.

The term “biological fluid” herein refers to a liquid taken from abiological source and includes, for example, blood, serum, plasma,sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears,saliva, and the like. As used herein, the terms “blood,” “plasma” and“serum” expressly encompass fractions or processed portions thereof.Similarly, where a sample is taken from a biopsy, swab, smear, etc., the“sample” expressly encompasses a processed fraction or portion derivedfrom the biopsy, swab, smear, etc.

The terms “maternal nucleic acids” and “fetal nucleic acids” hereinrefer to the nucleic acids of a pregnant female subject and the nucleicacids of the fetus being carried by the pregnant female, respectively.

As used herein, the term “corresponding to” sometimes refers to anucleic acid sequence e.g. a gene or a chromosome, that is present inthe genome of different subjects, and which does not necessarily havethe same sequence in all genomes, but serves to provide the identityrather than the genetic information of a sequence of interest e.g. agene or chromosome.

As used herein, the term “substantially cell free” encompassespreparations of the desired sample from which cell components that arenormally associated with it are removed. For example, a plasma sample isrendered substantially cell free by removing blood cells e.g. red cells,which are normally associated with it. In some embodiments,substantially free samples are processed to remove cells that wouldotherwise contribute to the desired genetic material that is to betested for a CNV.

As used herein, the term “fetal fraction” refers to the fraction offetal nucleic acids present in a sample comprising fetal and maternalnucleic acid. Fetal fraction is often used to characterize the cfDNA ina mother's blood.

As used herein the term “chromosome” refers to the heredity-bearing genecarrier of a living cell which is derived from chromatin and whichcomprises DNA and protein components (especially histones). Theconventional internationally recognized individual human genomechromosome numbering system is employed herein.

As used herein, the term “polynucleotide length” refers to the absolutenumber of nucleic acid molecules (nucleotides) in a sequence or in aregion of a reference genome. The term “chromosome length” refers to theknown length of the chromosome given in base pairs e.g. provided in theNCBI36/hg18 assembly of the human chromosome found on the world wide webat genome.ucsc.edu/cgi-bin/hgTracks?hgsid=167155613&chromInfoPage=

The term “subject” herein refers to a human subject as well as anon-human subject such as a mammal, an invertebrate, a vertebrate, afungus, a yeast, a bacteria, and a virus. Although the examples hereinconcern humans and the language is primarily directed to human concerns,the concepts disclosed herein are applicable to genomes from any plantor animal, and are useful in the fields of veterinary medicine, animalsciences, research laboratories and such.

The term “condition” herein refers to “medical condition” as a broadterm that includes all diseases and disorders, but can include[injuries] and normal health situations, such as pregnancy, that mightaffect a person's health, benefit from medical assistance, or haveimplications for medical treatments.

The term “complete” is used herein in reference to a chromosomalaneuploidy to refer to a gain or loss of an entire chromosome.

The term “partial” when used in reference to a chromosomal aneuploidyherein refers to a gain or loss of a portion i.e. segment, of achromosome.

The term “mosaic” herein refers to denote the presence of twopopulations of cells with different karyotypes in one individual who hasdeveloped from a single fertilized egg. Mosaicism may result from amutation during development which is propagated to only a subset of theadult cells.

The term “non-mosaic” herein refers to an organism e.g. a human fetus,composed of cells of one karyotype.

The term “using a chromosome” when used in reference to determining achromosome dose, herein refers to using the sequence informationobtained for a chromosome i.e. the number of sequence tags obtained fora chromosome.

The term “sensitivity” as used herein is equal to the number of truepositives divided by the sum of true positives and false negatives.

The term “specificity” as used herein is equal to the number of truenegatives divided by the sum of true negatives and false positives.

The term “hypodiploid” herein refers to a chromosome number that is oneor more lower than the normal haploid number of chromosomescharacteristic for the species.

A “polymorphic site” is a locus at which nucleotide sequence divergenceoccurs. The locus may be as small as one base pair. Illustrative markershave at least two alleles, each occurring at frequency of greater than1%, and more typically greater than 10% or 20% of a selected population.A polymorphic site may be the site of a single nucleotide polymorphism(SNP), a small-scale multi-base deletion or insertion, aMulti-Nucleotide Polymorphism (MNP) or a Short Tandem Repeat (STR). Theterms “polymorphic locus” and “polymorphic site” are herein usedinterchangeably.

A “polymorphic sequence” herein refers to a nucleic acid sequence e.g. aDNA sequence, that comprises one or more polymorphic sites e.g one SNPor a tandem SNP. Polymorphic sequences according to the presenttechnology can be used to specifically differentiate between maternaland non-maternal alleles in the maternal sample comprising a mixture offetal and maternal nucleic acids.

A “single nucleotide polymorphism” (SNP) as used herein occurs at apolymorphic site occupied by a single nucleotide, which is the site ofvariation between allelic sequences. The site is usually preceded by andfollowed by highly conserved sequences of the allele (e.g., sequencesthat vary in less than 1/100 or 1/1000 members of the populations). ASNP usually arises due to substitution of one nucleotide for another atthe polymorphic site. A transition is the replacement of one purine byanother purine or one pyrimidine by another pyrimidine. A transversionis the replacement of a purine by a pyrimidine or vice versa. SNPs canalso arise from a deletion of a nucleotide or an insertion of anucleotide relative to a reference allele. Single nucleotidepolymorphisms (SNPs) are positions at which two alternative bases occurat appreciable frequency (>1%) in the human population, and are the mostcommon type of human genetic variation.

The term “tandem SNPs” herein refers to two or more SNPs that arepresent within a polymorphic target nucleic acid sequence.

The term “short tandem repeat” or “STR” as used herein refers to a classof polymorphisms that occurs when a pattern of two or more nucleotidesare repeated and the repeated sequences are directly adjacent to eachother. The pattern can range in length from 2 to 10 base pairs (bp) (forexample (CATG)_(n) in a genomic region) and is typically in thenon-coding intron region. By examining several STR loci and counting howmany repeats of a specific STR sequence there are at a given locus, itis possible to create a unique genetic profile of an individual.

As used herein, the term “miniSTR” herein refers to tandem repeat offour or more base pairs that spans less than about 300 base pairs, lessthan about 250 base airs, less than about 200 base pairs, less thanabout 150 base pairs, less than about 100 base pairs, less than about 50base pairs, or less than about 25 base pairs. “miniSTRs” are STRs thatare amplifiable from cfDNA templates.

The terms “polymorphic target nucleic acid,” “polymorphic sequence,”“polymorphic target nucleic acid sequence” and “polymorphic nucleicacid” are used interchangeably herein to refer to a nucleic acidsequence (e.g. a DNA sequence) that comprises one or more polymorphicsites.

The term “plurality of polymorphic target nucleic acids” herein refersto a number of nucleic acid sequences each comprising at least onepolymorphic site, e.g. one SNP, such that at least 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 15, 20, 25, 30, 40 or more different polymorphic sites areamplified from the polymorphic target nucleic acids to identify and/orquantify fetal alleles present in maternal samples comprising fetal andmaternal nucleic acids.

The term “enrich” herein refers to the process of amplifying polymorphictarget nucleic acids contained in a portion of a maternal sample, andcombining the amplified product with the remainder of the maternalsample from which the portion was removed. For example, the remainder ofthe maternal sample can be the original maternal sample.

The term “original maternal sample” herein refers to a non-enrichedbiological sample obtained from a pregnant subject e.g. a woman, whoserves as the source from which a portion is removed to amplifypolymorphic target nucleic acids. The “original sample” can be anysample obtained from a pregnant subject, and the processed fractionsthereof e.g. a purified cfDNA sample extracted from a maternal plasmasample.

The term “primer,” as used herein refers to an isolated oligonucleotidewhich is capable of acting as a point of initiation of synthesis whenplaced under conditions in which synthesis of a primer extensionproduct, which is complementary to a nucleic acid strand, is induced(i.e., in the presence of nucleotides and an inducing agent such as DNApolymerase and at a suitable temperature and pH). The primer ispreferably single stranded for maximum efficiency in amplification, butmay alternatively be double stranded. If double stranded, the primer isfirst treated to separate its strands before being used to prepareextension products. Preferably, the primer is anoligodeoxyribonucleotide. The primer must be sufficiently long to primethe synthesis of extension products in the presence of the inducingagent. The exact lengths of the primers will depend on many factors,including temperature, source of primer, use of the method, and theparameters used for primer design.

The phrase “cause to be administered” refers to the actions taken by amedical professional (e.g., a physician), or a person controlling ordirecting medical care of a subject, that control and/or permit theadministration of the agent(s)/compound(s) at issue to the subject.Causing to be administered can involve diagnosis and/or determination ofan appropriate therapeutic or prophylactic regimen, and/or prescribingparticular agent(s)/compounds for a subject. Such prescribing caninclude, for example, drafting a prescription form, annotating a medicalrecord, and the like. Similarly, “cause to be performed”, e.g., for adiagnostic procedure refers to the actions taken by a medicalprofessional (e.g., a physician), or a person controlling or directingmedical care of a subject, that control and/or permit the performance ofone or more diagnostic protocols to or on the subject.

INTRODUCTION

Disclosed herein are methods, apparatus, and systems for determiningcopy number variations (CNV) of different sequences of interest in atest sample that comprises a mixture of nucleic acids derived from twodifferent genomes, and which are known or are suspected to differ in theamount of one or more sequence of interest. Copy number variationsdetermined by the methods and apparatus disclosed herein include gainsor losses of entire chromosomes, alterations involving very largechromosomal segments that are microscopically visible, and an abundanceof sub-microscopic copy number variation of DNA segments ranging fromkilobases (kb) to megabases (Mb) in size. In various embodiments, themethods comprise a machine-implemented statistical approach thataccounts for accrued variability stemming from process-related,interchromosomal and inter-sequencing variability. The method isapplicable to determining CNV of any fetal aneuploidy, and CNVs known orsuspected to be associated with a variety of medical conditions. CNVthat can be determined according to the present method include trisomiesand monosomies of any one or more of chromosomes 1-22, X and Y, otherchromosomal polysomies, and deletions and/or duplications of segments ofany one or more of the chromosomes, which can be detected by sequencingonly once the nucleic acids of a test sample. Any aneuploidy can bedetermined from sequencing information that is obtained by sequencingonly once the nucleic acids of a test sample.

CNV in the human genome significantly influence human diversity andpredisposition to disease (Redon et al., Nature 23:444-454 [2006],Shaikh et al. Genome Res 19:1682-1690 [2009]). CNVs have been known tocontribute to genetic disease through different mechanisms, resulting ineither imbalance of gene dosage or gene disruption in most cases. Inaddition to their direct correlation with genetic disorders, CNVs areknown to mediate phenotypic changes that can be deleterious. Recently,several studies have reported an increased burden of rare or de novoCNVs in complex disorders such as Autism, ADHD, and schizophrenia ascompared to normal controls, highlighting the potential pathogenicity ofrare or unique CNVs (Sebat et al., 316:445-449 [2007]; Walsh et al.,Science 320:539-543 [2008]). CNV arise from genomic rearrangements,primarily owing to deletion, duplication, insertion, and unbalancedtranslocation events.

The methods and apparatus described herein may employ next generationsequencing technology (NGS), which is massively parallel sequencing. Incertain embodiments, clonally amplified DNA templates or single DNAmolecules are sequenced in a massively parallel fashion within a flowcell (e.g. as described in Volkerding et al. Clin Chem 55:641-658[2009]; Metzker M Nature Rev 11:31-46 [2010]). In addition tohigh-throughput sequence information, NGS provides quantitativeinformation, in that each sequence read is a countable “sequence tag”representing an individual clonal DNA template or a single DNA molecule.The sequencing technologies of NGS include pyrosequencing,sequencing-by-synthesis with reversible dye terminators, sequencing byoligonucleotide probe ligation and ion semiconductor sequencing. DNAfrom individual samples can be sequenced individually (i.e. singleplexsequencing) or DNA from multiple samples can be pooled and sequenced asindexed genomic molecules (i.e. multiplex sequencing) on a singlesequencing run, to generate up to several hundred million reads of DNAsequences. Examples of sequencing technologies that can be used toobtain the sequence information according to the present method aredescribed below.

In some embodiments, the methods and apparatus disclosed herein mayemploy the following some or all of the operations from the followingsequence: obtain a nucleic acid test sample from a patient (typically bya non-invasive procedure); process the test sample in preparation forsequencing; sequence nucleic acids from the test sample to producenumerous reads (e.g., at least 10,000); align the reads to portions of areference sequence/genome and determine the amount of DNA (e.g., thenumber of reads) that map to defined portions the reference sequence(e.g., to defined chromosomes or chromosome segments); calculate a doseof one or more of the defined portions by normalizing the amount of DNAmapping to the defined portions with an amount of DNA mapping to one ormore normalizing chromosomes or chromosome segments selected for thedefined portion; determining whether the dose indicates that the definedportion is “affected” (e.g., aneuploidy or mosaic); reporting thedetermination and optionally converting it to a diagnosis; using thediagnosis or determination to develop a plan of treatment, monitoring,or further testing for the patient.

Determination of Normalizing Sequences in Qualified Samples: NormalizingChromosome Sequences and Normalizing Segment Sequences

Normalizing sequences are identified using sequence information from aset of qualified samples obtained from subjects known to comprise cellshaving a normal copy number for any one sequence of interest e.g. achromosome or segment thereof. Determination of normalizing sequences isoutlined in steps 110, 120, 130, 140, and 145 of the embodiment of themethod depicted in FIG. 1. The sequence information obtained from thequalified samples is used for determining statistically meaningfulidentification of chromosomal aneuploidies in test samples (step 165FIG. 1, and Examples).

FIG. 1 provides a flow diagram 100 of an embodiment for determining aCNV of a sequence of interest e.g. a chromosome or segment thereof, in abiological sample. In some embodiments, a biological sample is obtainedfrom a subject and comprises a mixture of nucleic acids contributed bydifferent genomes. The different genomes can be contributed to thesample by two individuals e.g. the different genomes are contributed bythe fetus and the mother carrying the fetus. Alternatively, the genomesare contributed to the sample by aneuploid cancerous cells and normaleuploid cells from the same subject e.g. a plasma sample from a cancerpatient.

Apart from analyzing a patient's test sample, one or more normalizingchromosomes or one or more normalizing chromosome segments are selectedfor each possible chromosome of interest. The normalizing chromosomes orsegments are identified asynchronously from the normal testing ofpatient samples, which may take place in a clinical setting. In otherwords, the normalizing chromosomes or segments are identified prior totesting patient samples. The associations between normalizingchromosomes or segments and chromosomes or segments of interest arestored for use during testing. As explained below, such association istypically maintained over periods of time that span testing of manysamples. The following discussion concerns embodiments for selectingnormalizing chromosomes or chromosome segments for individualchromosomes or segments of interest.

A set of qualified samples is obtained to identify qualified normalizingsequences and to provide variance values for use in determiningstatistically meaningful identification of CNV in test samples. In step110, a plurality of biological qualified samples are obtained from aplurality of subjects known to comprise cells having a normal copynumber for any one sequence of interest. In one embodiment, thequalified samples are obtained from mothers pregnant with a fetus thathas been confirmed using cytogenetic means to have a normal copy numberof chromosomes. The biological qualified samples may be a biologicalfluid e.g. plasma, or any suitable sample as described below. In someembodiments, a qualified sample contains a mixture of nucleic acidmolecules e.g. cfDNA molecules. In some embodiments, the qualifiedsample is a maternal plasma sample that contains a mixture of fetal andmaternal cfDNA molecules. Sequence information for normalizingchromosomes and/or segments thereof is obtained by sequencing at least aportion of the nucleic acids e.g. fetal and maternal nucleic acids,using any known sequencing method. Preferably, any one of the NextGeneration Sequencing (NGS) methods described elsewhere herein is usedto sequence the fetal and maternal nucleic acids as single or clonallyamplified molecules. In various embodiments, the qualified samples areprocessed as disclosed below prior to and during sequencing. They may beprocessed using appraratus, systems, and kits as disclosed herein.

In step 120, at least a portion of each of all the qualified nucleicacids contained in the qualified samples are sequenced to generatemillions of sequence reads e.g. 36 bp reads, which are aligned to areference genome, e.g. hg18. In some embodiments, the sequence readscomprise about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp,about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about95 bp, about 100 bp, about 110 bp, about 120 bp, about 130, about 140bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350bp, about 400 bp, about 450 bp, or about 500 bp. It is expected thattechnological advances will enable single-end reads of greater than 500bp enabling for reads of greater than about 1000 bp when paired endreads are generated. In one embodiment, the mapped sequence readscomprise 36 bp. In another embodiment, the mapped sequence readscomprise 25 bp. Sequence reads are aligned to a reference genome, andthe reads that are uniquely mapped to the reference genome are known assequence tags. In one embodiment, at least about 3×10⁶ qualifiedsequence tags, at least about 5×10⁶ qualified sequence tags, at leastabout 8×10⁶ qualified sequence tags, at least about 10×10⁶ qualifiedsequence tags, at least about 15×10⁶ qualified sequence tags, at leastabout 20×10⁶ qualified sequence tags, at least about 30×10⁶ qualifiedsequence tags, at least about 40×10⁶ qualified sequence tags, or atleast about 50×10⁶ qualified sequence tags comprising between 20 and 40bp reads are obtained from reads that map uniquely to a referencegenome.

In step 130, all the tags obtained from sequencing the nucleic acids inthe qualified samples are counted to determine a qualified sequence tagdensity. In one embodiment the sequence tag density is determined as thenumber of qualified sequence tags mapped to the sequence of interest onthe reference genome. In another embodiment, the qualified sequence tagdensity is determined as the number of qualified sequence tags mapped toa sequence of interest normalized to the length of the qualifiedsequence of interest to which they are mapped. Sequence tag densitiesthat are determined as a ratio of the tag density relative to the lengthof the sequence of interest are herein referred to as tag densityratios. Normalization to the length of the sequence of interest is notrequired, and may be included as a step to reduce the number of digitsin a number to simplify it for human interpretation. As all qualifiedsequence tags are mapped and counted in each of the qualified samples,the sequence tag density for a sequence of interest e.g. aclinically-relevant sequence, in the qualified samples is determined, asare the sequence tag densities for additional sequences from whichnormalizing sequences are identified subsequently.

In some embodiments, the sequence of interest is a chromosome that isassociated with a complete chromosomal aneuploidy e.g. chromosome 21,and the qualified normalizing sequence is a complete chromosome that isnot associated with a chromosomal aneuploidy and whose variation insequence tag density approximates that of the sequence (i.e. chromosome)of interest e.g. chromosome 21. The selected normalizing chromosome(s)may the one or group that best approximates the variation in sequencetag density of the sequence of interest. Any one or more of chromosomes1-22, X, and Y can be a sequence of interest, and one or morechromosomes can be identified as the normalizing sequence for each ofthe any one chromosomes 1-22, X and Y in the qualified samples. Thenormalizing chromosome can be an individual chromosome or it can be agroup of chromosomes as described elsewhere herein.

In another embodiment, the sequence of interest is a segment of achromosome associated with a partial aneuploidy, e.g. a chromosomaldeletion or insertion, or unbalanced chromosomal translocation, and thenormalizing sequence is a chromosomal segment (or group of segments)that is not associated with the partial aneuploidy and whose variationin sequence tag density approximates that of the chromosome segmentassociated with the partial aneuploidy. The selected normalizingchromosome segment(s) may the one or more that best approximates thevariation in sequence tag density of the sequence of interest. Any oneor more segments of any one or more chromosomes 1-22, X, and Y can be asequence of interest.

In other embodiments, the sequence of interest is a segment of achromosome associated with a partial aneuploidy and the normalizingsequence is a whole chromosome or chromosomes. In still otherembodiments, the sequence of interest is a whole chromosome associatedwith an aneuploidy and the normalizing sequence is a chromosomal segmentor segments that is not associated with the aneuploidy.

Whether a single sequence or a group of sequences are identified in thequalified samples as the normalizing sequence(s) for any one or moresequences of interest, the qualified normalizing sequence may be chosento have a variation in sequence tag density that best or effectivelyapproximates that of the sequence of interest as determined in thequalified samples. For example, a qualified normalizing sequence is asequence that produces the smallest variability across the qualifiedsamples when used to normalize the sequence of interest, i.e. thevariability of the normalizing sequence is closest to that of thesequence of interest determined in qualified samples. Stated anotherway, the qualified normalizing sequence is the sequence selected toproduce the least variation in sequence dose (for the sequence ofinterest) across the qualified samples. Thus, the process selects asequence that when used as a normalizing chromosome is expected toproduce the smallest variability in run-to-run chromosome dose for thesequence of interest.

The normalizing sequence identified in the qualified samples for any oneor more sequences of interest remains the normalizing sequence of choicefor determining the presence or absence of aneuploidy in test samplesover days, weeks, months, and possibly years, provided that proceduresneeded to generate sequencing libraries, and sequencing the samples areessentially unaltered over time. As described above, normalizingsequences for determining the presence of aneuploidies are chosen for(possibly among other reasons as well) the variability in the number ofsequence tags that are mapped to it among samples e.g. differentsamples, and sequencing runs e.g. sequencing runs that occur on the sameday and/or different days, that best approximates the variability of thesequence of interest for which it is used as a normalizing parameter.Substantial alterations in these procedures will affect the number oftags that are mapped to all sequences, which in turn will determinewhich one or group of sequences will have a variability across samplesin the same and/or in different sequencing runs, on the same day or ondifferent days that most closely approximates that of the sequence(s) ofinterest, which would require that the set of normalizing sequences bere-determined. Substantial alterations in procedures include changes inthe laboratory protocol used for preparing the sequencing library, whichincludes changes related to preparing samples for multiplex sequencinginstead of singleplex sequencing, and changes in sequencing platforms,which include changes in the chemistry used for sequencing.

In some embodiments, the normalizing sequence chosen to normalize aparticular sequence of interest is a sequence that best distinguishesone or more qualified, samples from one or more affected samples, whichimplies that the normalizing sequence is a sequence that has thegreatest differentiability i.e. the differentiability of the normalizingsequence is such that it provides optimal differentiation to a sequenceof interest in an affected test sample to easily distinguish theaffected test sample from other unaffected samples. In otherembodiments, the normalizing sequence is a sequence that has acombination of the smallest variability and the greatestdifferentiability.

The level of differentiability can be determined as a statisticaldifference between the sequence doses e.g. chromosome doses or segmentdoses, in a population of qualified samples and the chromosome dose(s)in one or more test samples as described below and shown in theExamples. For example, differentiability can be represented numericallyas a T-test value, which represents the statistical difference betweenthe chromosome doses in a population of qualified samples and thechromosome dose(s) in one or more test samples. Alternatively,differentiability can be represented numerically as a NormalizedChromosome Value (NCV), which is a z-score for chromosome doses as longas the distribution for the NCV is normal. Similarly, differentiabilitycan be represented numerically as a T-test value, which represents thestatistical difference between the segment doses in a population ofqualified samples and the segment dose(s) in one or more test samples.In the case where chromosome segments are the sequences of interest,differentiability of segment doses can be represented numerically as aNormalized Segment Value (NSV), which is a z-score for chromosomesegment doses as long as the distribution for the NSV is normal. Indetermining the z-score, the mean and standard deviation of chromosomeor segment doses in a set of qualified samples can be used.Alternatively, the mean and standard deviation of chromosome or segmentdoses in a training set comprising qualified samples and affectedsamples can be used. In other embodiments, the normalizing sequence is asequence that has the smallest variability and the greatestdifferentiability or an optimal combination of small variability andlarge differentiability.

The method identifies sequences that inherently have similarcharacteristics and that are prone to similar variations among samplesand sequencing runs, and which are useful for determining sequence dosesin test samples.

Determination of Sequence Doses (i.e. Chromosome Doses or Segment Doses)in Qualified Samples

In step 140, based on the calculated qualified tag densities, aqualified sequence dose i.e. a chromosome dose or a segment dose, for asequence of interest is determined as the ratio of the sequence tagdensity for the sequence of interest and the qualified sequence tagdensity for additional sequences from which normalizing sequences areidentified subsequently in step 145. The identified normalizingsequences are used subsequently to determine sequence doses in testsamples.

In one embodiment, the sequence dose in the qualified samples is achromosome dose that is calculated as the ratio of the number ofsequence tags for a chromosome of interest and the number of sequencetags for a normalizing chromosome sequence in a qualified sample. Thenormalizing chromosome sequence can be a single chromosome, a group ofchromosomes, a segment of one chromosome, or a group of segments fromdifferent chromosomes. Accordingly, a chromosome dose for a chromosomeof interest is determined in a qualified sample as (i) the ratio of thenumber of tags for a chromosome of interest and the number of tags for anormalizing chromosome sequence composed of a single chromosome, (ii)the ratio of the number of tags for a chromosome of interest and thenumber of tags for a normalizing chromosome sequence composed of two ormore chromosomes, (iii) the ratio of the number of tags for a chromosomeof interest and the number of tags for a normalizing segment sequencecomposed of a single segment of a chromosome, (iv) the ratio of thenumber of tags for a chromosome of interest and the number of tags for anormalizing segment sequence composed of two or more segments form onechromosome, or (v) the ratio of the number of tags for a chromosome ofinterest and the number of tags for a normalizing segment sequencecomposed of two or more segments of two or more chromosomes. Examplesfor determining a chromosome dose for chromosome of interest 21according to (i)-(v) are as follows: chromosome doses for chromosome ofinterest e.g. chromosome 21, are determined as a ratio of the sequencetag density of chromosome 21 and the sequence tag density for each ofall the remaining chromosomes i.e. chromosomes 1-20, chromosome 22,chromosome X, and chromosome Y (i); chromosome doses for chromosome ofinterest e.g. chromosome 21, are determined as a ratio of the sequencetag density of chromosome 21 and the sequence tag density for allpossible combinations of two or more remaining chromosomes (ii);chromosome doses for chromosome of interest e.g. chromosome 21, aredetermined as a ratio of the sequence tag density of chromosome 21 andthe sequence tag density for a segment of another chromosome e.g.chromosome 9 (iii); chromosome doses for chromosome of interest e.g.chromosome 21, are determined as a ratio of the sequence tag density ofchromosome 21 and the sequence tag density for two segment of one otherchromosome e.g. two segments of chromosome 9 (iv); and chromosome dosesfor chromosome of interest e.g. chromosome 21, are determined as a ratioof the sequence tag density of chromosome 21 and the sequence tagdensity for two segments of two different chromosomes e.g. a segment ofchromosome 9 and a segment of chromosome 14.

In another embodiment, the sequence dose in the qualified samples is asegment dose that is calculated as the ratio of the number of sequencetags for a segment of interest, that is not a whole chromosome, and thenumber of sequence tags for a normalizing segment sequence in aqualified sample. The normalizing segment sequence can be, for example,a whole chromosome, a group of whole chromosomes, a segment of onechromosome, or a group of segments from different chromosomes. Forexample, a segment dose for a segment of interest is determined in aqualified sample as (i) the ratio of the number of tags for a segment ofinterest and the number of tags for a normalizing segment sequencecomposed of a single segment of a chromosome, (ii) the ratio of thenumber of tags for a segment of interest and the number of tags for anormalizing segment sequence composed of two or more segments of onechromosome, or (iii) the ratio of the number of tags for a segment ofinterest and the number of tags for a normalizing segment sequencecomposed of two or more segments of two or more different chromosomes.

Chromosome doses for one or more chromosomes of interest are determinedin all qualified samples, and a normalizing chromosome sequence isidentified in step 145. Similarly, segment doses for one or moresegments of interest are determined in all qualified samples, and anormalizing segment sequence is identified in step 145.

Identification of Normalizing Sequences from Qualified Sequence Doses

In step 145, a normalizing sequence is identified for a sequence ofinterest as the sequence based on the calculated sequence doses e.g.,that results in the smallest variability in sequence dose for thesequence of interest across all qualified samples. The method identifiessequences that inherently have similar characteristics and that areprone to similar variations among samples and sequencing runs, and whichare useful for determining sequence doses in test samples.

Normalizing sequences for one or more sequences of interest can beidentified in a set of qualified samples, and the sequences that areidentified in the qualified samples are used subsequently to calculatesequence doses for one or more sequences of interest in each of the testsamples (step 150) to determine the presence or absence of aneuploidy ineach of the test samples. The normalizing sequence identified forchromosomes or segments of interest may differ when different sequencingplatforms are used and/or when differences exist in the purification ofthe nucleic acid that is to be sequenced and/or preparation of thesequencing library. The use of normalizing sequences according to themethods described herein provides specific and sensitive measure of avariation in copy number of a chromosome or segment thereof irrespectiveof sample preparation and/or sequencing platform that is used.

In some embodiments, more than one normalizing sequence is identifiedi.e. different normalizing sequences can be determined for one sequenceof interest, and multiple sequence doses can be determined for onesequence of interest. For example, the variation, e.g. coefficient ofvariation, in chromosome dose for chromosome of interest 21 is leastwhen the sequence tag density of chromosome 14 is used. However, two,three, four, five, six, seven, eight or more normalizing sequences canbe identified for use in determining a sequence dose for a sequence ofinterest in a test sample. As an example, a second dose for chromosome21 in any one test sample can be determined using chromosome 7,chromosome 9, chromosome 11 or chromosome 12 as the normalizingchromosome sequence as these chromosomes all have CV close to that forchromosome 14 (see Example 8, Table 10). Preferably, when a singlechromosome is chosen as the normalizing chromosome sequence for achromosome of interest, the normalizing chromosome sequence will be achromosome that results in chromosome doses for the chromosome ofinterest that has the smallest variability across all samples testede.g. qualified samples.

Normalizing Chromosome Sequence as a Normalizing Sequence forChromosome(s)

In other embodiments, a normalizing chromosome sequence can be a singlesequence or it can be a group of sequences. For example, in someembodiments, a normalizing sequence is a group of sequences e.g. a groupof chromosomes, that is identified as the normalizing sequence for anyor more of chromosomes 1-22, X and Y. The group of chromosomes thatcompose the normalizing sequence for a chromosome of interest i.e. anormalizing chromosome sequence, can be a group of two, three, four,five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, ortwenty-two chromosomes, and including or excluding one or both ofchromosomes X, and Y. The group of chromosomes that is identified as thenormalizing chromosome sequence is a group of chromosomes that resultsin chromosome doses for the chromosome of interest that has the smallestvariability across all samples tested e.g. qualified samples.Preferably, individual and groups of chromosomes are tested together fortheir ability to best mimic the behavior of the sequence of interest forwhich they are chosen as normalizing chromosome sequences.

In one embodiment, the normalizing sequence for chromosome 21 isselected from chromosome 9, chromosome 1, chromosome 2, chromosome 3,chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8,chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome14, chromosome 15, chromosome 16, and chromosome 17. In anotherembodiment, the normalizing sequence for chromosome 21 is selected fromchromosome 9, chromosome 1, chromosome 2, chromosome 11, chromosome 12,and chromosome 14. Alternatively, the normalizing sequence forchromosome 21 is a group of chromosomes selected from chromosome 9,chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5,chromosome 6, chromosome 7, chromosome 8, chromosome 10, chromosome 11,chromosome 12, chromosome 13, chromosome 14, chromosome 15, chromosome16, and chromosome 17. In another embodiment, the group of chromosomesis a group selected from chromosome 9, chromosome 1, chromosome 2,chromosome 11, chromosome 12, and chromosome 14.

In some embodiments, the method is further improved by using anormalizing sequence that is determined by systematic calculation of allchromosome doses using each chromosome individually and in all possiblecombinations with all remaining chromosomes (see Example 13). Forexample, a systematically determined normalizing chromosome can bedetermined for each chromosome of interest by systematically calculatingall possible chromosome doses using one of any of chromosomes 1-22, X,and Y, and combinations of two or more of chromosomes 1-22, X, and Y todetermine which single or group of chromosomes is the normalizingchromosome that results in the least variability of the chromosome dosefor a chromosome of interest across a set of qualified samples (seeExample 13). Accordingly, in one embodiment, the systematicallycalculated normalizing chromosome sequence for chromosome 21 is a groupof chromosomes consisting of chromosome 4, chromosome 14, chromosome 16,chromosome 20, and chromosome 22. Single or groups of chromosomes can bedetermined for all chromosomes in the genome.

In one embodiment, the normalizing sequence for chromosome 18 isselected chromosome 8, chromosome 2, chromosome 3, chromosome 4,chromosome 5, chromosome 6, chromosome 7, chromosome 9, chromosome 10,chromosome 11, chromosome 12, chromosome 13, and chromosome 14.Preferably, the normalizing sequence for chromosome 18 is selected fromchromosome 8, chromosome 2, chromosome 3, chromosome 5, chromosome 6,chromosome 12, and chromosome 14. Alternatively, the normalizingsequence for chromosome 18 is a group of chromosomes selected fromchromosome 8, chromosome 2, chromosome 3, chromosome 4, chromosome 5,chromosome 6, chromosome 7, chromosome 9, chromosome 10, chromosome 11,chromosome 12, chromosome 13, and chromosome 14. Preferably, the groupof chromosomes is a group selected from chromosome 8, chromosome 2,chromosome 3, chromosome 5, chromosome 6, chromosome 12, and chromosome14.

In another embodiment, the normalizing sequence for chromosome 18 isdetermined by systematic calculation of all possible chromosome dosesusing each possible normalizing chromosome individually and all possiblecombinations of normalizing chromosomes (as explained elsewhere herein).Accordingly, in one embodiment, the normalizing sequence for chromosome18 is a normalizing chromosome consisting of the group of chromosomesconsisting of chromosome 2, chromosome 3, chromosome 5, and chromosome7.

In one embodiment, the normalizing sequence for chromosome X is selectedfrom chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome10, chromosome 11, chromosome 12, chromosome 13, chromosome 14,chromosome 15, and chromosome 16. Preferably, the normalizing sequencefor chromosome X is selected from chromosome 2, chromosome 3, chromosome4, chromosome 5, chromosome 6 and chromosome 8. Alternatively, thenormalizing sequence for chromosome X is a group of chromosomes selectedfrom chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome10, chromosome 11, chromosome 12, chromosome 13, chromosome 14,chromosome 15, and chromosome 16. Preferably, the group of chromosomesis a group selected from chromosome 2, chromosome 3, chromosome 4,chromosome 5, chromosome 6, and chromosome 8.

In another embodiment, the normalizing sequence for chromosome X isdetermined by systematic calculation of all possible chromosome dosesusing each possible normalizing chromosome individually and all possiblecombinations of normalizing chromosomes (as explained elsewhere herein).Accordingly, in one embodiment, the normalizing sequence for chromosomeX is a normalizing chromosome consisting of the group of chromosome 4and chromosome 8.

In one embodiment, the normalizing sequence for chromosome 13 is achromosome selected from chromosome 2, chromosome 3, chromosome 4,chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9,chromosome 10, chromosome 11, chromosome 12, chromosome 14, chromosome18, and chromosome 21. Preferably, the normalizing sequence forchromosome 13 is a chromosome selected from chromosome 2, chromosome 3,chromosome 4, chromosome 5, chromosome 6, and chromosome 8. In anotherembodiment, the normalizing sequence for chromosome 13 is a group ofchromosomes selected from chromosome 2, chromosome 3, chromosome 4,chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9,chromosome 10, chromosome 11, chromosome 12, chromosome 14, chromosome18, and chromosome 21. Preferably, the group of chromosomes is a groupselected from chromosome 2, chromosome 3, chromosome 4, chromosome 5,chromosome 6, and chromosome 8.

In another embodiment, the normalizing sequence for chromosome 13 isdetermined by systematic calculation of all possible chromosome dosesusing each possible normalizing chromosome individually and all possiblecombinations of normalizing chromosomes (as explained elsewhere herein).Accordingly, in one embodiment, the normalizing sequence for chromosome13 is a normalizing chromosome comprising the group of chromosome 4 andchromosome 5. In another embodiment, the normalizing sequence forchromosome 13 is a normalizing chromosome consisting of the group ofchromosome 4 and chromosome 5.

The variation in chromosome dose for chromosome Y is greater than 30independently of which normalizing chromosome is used in determining thechromosome Y dose. Therefore, any one chromosome, or a group of two ormore chromosomes selected from chromosomes 1-22 and chromosome X can beused as the normalizing sequence for chromosome Y. In one embodiment,the at least one normalizing chromosome is a group of chromosomesconsisting of chromosomes 1-22, and chromosome X. In another embodiment,the group of chromosomes consists of chromosome 2, chromosome 3,chromosome 4, chromosome 5, and chromosome 6.

In another embodiment, the normalizing sequence for chromosome Y isdetermined by systematic calculation of all possible chromosome dosesusing each possible normalizing chromosome individually and all possiblecombinations of normalizing chromosomes (as explained elsewhere herein).Accordingly, in one embodiment, the normalizing sequence for chromosomeY is a normalizing chromosome comprising the group of chromosomesconsisting of chromosome 4 and chromosome 6. In another embodiment, thenormalizing sequence for chromosome Y is a normalizing chromosomeconsisting of the group of chromosomes consisting of chromosome 4 andchromosome 6.

The normalizing sequence used to calculate the dose of differentchromosomes of interest, or of different segments of interest can be thesame or it can be a different normalizing sequence for differentchromosomes or segments of interest, respectively. For example, thenormalizing sequence e.g. a normalizing chromosome (one or a group) forchromosome of interest A can be the same or it can be different from thenormalizing sequence e.g. a normalizing chromosome (one or a group) forchromosome of interest B.

The normalizing sequence for a complete chromosome may be a completechromosome or a group of complete chromosomes, or it may be a segment ofa chromosome, or a group of segments of one or more chromosomes.

Normalizing Segment Sequence as a Normalizing Sequence for Chromosome(s)

In another embodiment, the normalizing sequence for a chromosome can bea normalizing segment sequence. The normalizing segment sequence can bea single segment or it can be a group of segments of one chromosome, orthey can be segments from two or more different chromosomes. Anormalizing segment sequence can be determined by systematic calculationof all combinations of segment sequences in the genome. For example, anormalizing segment sequence for chromosome 21 can be a single segmentthat is bigger or smaller than the size of chromosome 21, which isapproximately 47 Mbp (million base pairs), for example, the normalizingsegment can be a segment from chromosome 9, which is approximately 140Mbp. Alternatively, a normalizing sequence for chromosome 21 can be forexample, a combination of segment sequences from two differentchromosome e.g. from chromosome 1, and from chromosome 12.

In one embodiment, the normalizing sequence for chromosome 21 is anormalizing segment sequence of one segment or of a group of two or moresegments of chromosomes 1-20, 22, X, and Y. In another embodiment, thenormalizing sequence for chromosome 18 is a segment or groups segmentsof chromosomes 1-17, 19-22, X, and Y. In another embodiment, thenormalizing sequence for chromosome 13 is a segment or groups ofsegments of chromosomes 1-12, 14-22, X, and Y. In another embodiment,the normalizing sequence for chromosome X is a segment or groupssegments of chromosomes 1-22, and Y. In another embodiment, thenormalizing sequence for chromosome Y is a segment or group of segmentsof chromosomes 1-22, and X. Normalizing segment sequences of single orgroups of segments can be determined for all chromosomes in the genome.The two or more segments of a normalizing segment sequence can besegments from one chromosome, or the two or more segments can besegments of two or more different chromosomes. As described fornormalizing chromosome sequences, a normalizing segment sequence can bethe same for two or more different chromosomes.

Normalizing Segment Sequence as a Normalizing Sequence for ChromosomeSegment(s)

The presence or absence of CNV of a sequence of interest can bedetermined when the sequence of interest is a segment of a chromosome.Variation in the copy number of a chromosome segment allows fordetermining the presence or absence of a partial chromosomal aneuploidy.Described below are examples of partial chromosomal aneuploidies thatare associated with various fetal abnormalities and disease conditions.The segment of the chromosome can be of any length. For example, it canrange from a kilobase to hundreds of megabases. The human genomeoccupies just over 3 billion DNA bases, which can be divided into tens,thousands, hundreds of thousands and millions of segments of differentsizes of which the copy number can be determined according to thepresent method. The normalizing sequence for a segment of a chromosomeis a normalizing segment sequence, which can be a single segment fromany one of the chromosomes 1-22, X and Y, or it can be a group ofsegments from any one or more of chromosomes 1-22, X, and Y.

The normalizing sequence for a segment of interest is a sequence thathas a variability across chromosomes and across samples that is closestto that of the segment of interest. Determination of a normalizingsequence can be performed as described for determining the normalizingsequence for a chromosome of interest when the normalizing sequence is agroup of segments of any one or more of chromosomes 1-22, X and Y. Anormalizing segment sequence of one or a group of segments can beidentified by calculating segment doses using one, and all possiblecombinations of two or more segments as normalizing sequences for thesegment of interest in each sample of a set of qualified samples i.e.samples known to be diploid for the segment of interest, and thenormalizing sequence is determined as that providing a segment dosehaving the lowest variability for the segment of interest across allqualified samples, as is described above for normalizing chromosomesequences.

For example, for a segment of interest that is 1 Mb (megabase), theremaining 3 million segments (minus the 1 mg segment of interest) of theapproximately 3 Gb human genome can be used individually or incombination with each other to calculate segment doses for a segment ofinterest in a qualified set of sample to determine which one or group ofsegments would serve as the normalizing segment sequence for qualifiedand test samples. Segments of interest can vary from about 1000 bases totens of megabases. Normalizing segment sequences can be composed of oneor more segments of the same size as that of the sequence of interest.In other embodiment, the normalizing segment sequence can be composed ofsegments that differ from that of the sequence of interest, and/or fromeach other. For example, a normalizing segment sequence for a 100,000base long sequence can be 20,000 bases long, and comprise a combinationof sequences of different lengths e.g. a 7,000+8,000+5,000 bases. As isdescribed elsewhere herein for normalizing chromosome sequences,normalizing segment sequences can be determined by systematiccalculation of all possible chromosome and/or segment doses using eachpossible normalizing chromosome segment individually and all possiblecombinations of normalizing segments (as explained elsewhere herein).Single or groups of segments can be determined for all segments and/orchromosomes in the genome.

The normalizing sequence used to calculate the dose of differentchromosome segments of interest can be the same or it can be a differentnormalizing sequence for different chromosome segments of interest. Forexample, the normalizing sequence e.g. a normalizing segment (one or agroup) for chromosome segment of interest A can be the same or it can bedifferent from the normalizing sequence e.g. a normalizing segment (oneor a group) for chromosome segment of interest B.

Normalizing Chromosome Sequence as a Normalizing Sequence for ChromosomeSegment(s)

In another embodiment, variations in copy number of chromosome segmentscan be determined using a normalizing chromosome, which can be a singlechromosome or a group of chromosomes as described above. The normalizingchromosome sequence can be the normalizing chromosome or group ofchromosomes that are identified for the chromosome of interest in a setof qualified samples by systematically determining which one or group ofchromosomes provide the lowest variability in the chromosome dose in aset of qualified samples. For example, to determine the presence orabsence of a partial deletion of chromosome 7, the normalizingchromosome or group of chromosomes that is used in the analysis for thepartial deletion is the chromosome or group of chromosomes that arefirst identified in a qualified set of samples as the normalizingsequence that provides the lowest chromosome dose for the entirechromosome 7. As is described elsewhere herein for normalizingchromosome sequences for chromosomes of interest, normalizing chromosomesequences for chromosome segments can be determined by systematiccalculation of all possible chromosome doses using each possiblenormalizing chromosome individually and all possible combinations ofnormalizing chromosomes (as explained elsewhere herein). Single orgroups of chromosomes can be determined for all segments of chromosomesin the genome. Examples demonstrating the use of normalizing chromosomesfor determining the presence of a partial chromosomal deletion and for apartial chromosomal duplication are provided as Examples 17 and 18.

In some embodiments, determination of a CNV of a chromosome segment isperformed by first subdividing the chromosome of interest into sectionsor bins of variable length. The bin length can be of at least about 1kbp, at least about 10 kbp, at least about 100 kbp, at least about 1mbp, at least about 10 mbp, or at least about 100 mbp. The smaller thebin length, the greater the resolution that is obtained to localize theCNV of the segment in the chromosome of interest.

Determining the presence or absence of a CNV of a segment of achromosome of interest can be obtained by comparing the dose for each ofthe bins of the chromosome of interest in a test sample to a the meanfor the corresponding bin dose determined for each bin of equivalentlength in a set of qualified samples. A normalized bin value for eachbin can be calculated as described above for the normalized segmentvalue as a normalized bin value (NBV), which relates the bin dose in atest sample to the mean of the of the corresponding bin dose in a set ofqualified samples. The NBV is calculated as:

${NBV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-th bindose in a set of qualified samples, and x_(ij) is the observed j-th bindose for test sample

Determination of Aneuploidies in Test Samples

Based on the identification of the normalizing sequence(s) in qualifiedsamples, a sequence dose is determined for a sequence of interest in atest sample comprising a mixture of nucleic acids derived from genomesthat differ in one or more sequences of interest.

In step 115, a test sample is obtained from a subject suspected or knownto carry a clinically-relevant CNV of a sequence of interest. The testsample may be a biological fluid e.g. plasma, or any suitable sample asdescribed below. As explained, the sample may be obtained using anon-invasive procedure such as a simple blood draw. In some embodiments,a test sample contains a mixture of nucleic acid molecules e.g. cfDNAmolecules. In some embodiments, the test sample is a maternal plasmasample that contains a mixture of fetal and maternal cfDNA molecules.

In step 125, at least a portion of the test nucleic acids in the testsample is sequenced as described for the qualified samples to generatemillions of sequence reads e.g. 36 bp reads. As in step 120, the readsgenerated from sequencing the nucleic acids in the test sample areuniquely mapped or aligned to a reference genome to produce tags. Asdescribed in step 120, at least about 3×10⁶ qualified sequence tags, atleast about 5×10⁶ qualified sequence tags, at least about 8×10⁶qualified sequence tags, at least about 10×10⁶ qualified sequence tags,at least about 15×10⁶ qualified sequence tags, at least about 20×10⁶qualified sequence tags, at least about 30×10⁶ qualified sequence tags,at least about 40×10⁶ qualified sequence tags, or at least about 50×10⁶qualified sequence tags comprising between 20 and 40 bp reads areobtained from reads that map uniquely to a reference genome. In certainembodiments, the reads produced by sequencing apparatus are provided inan electronic format. Alignment is accomplished using computationalapparatus as discussed below. Individual reads are compared against thereference genome, which is often vast (millions of base pairs) toidentify sites where the reads uniquely correspond with the referencegenome. In some embodiments, the alignment procedure permits limitedmismatch between reads and the reference genome. In some cases, 1, 2, or3 base pairs in a read are permitted to mismatch corresponding basepairs in a reference genome, and yet a mapping is still made.

In step 135, all or most of the tags obtained from sequencing thenucleic acids in the test samples are counted to determine a testsequence tag density using a computational apparatus as described below.In some embodiments, each read is aligned to a particular region of thereference genome (a chromosome or segment in most cases), and the readis converted to a tag by appending site information to the read. As thisprocess unfolds, the computational apparatus may keep a running count ofthe number of tags/reads mapping to each region of the reference genome(chromosome or segment in most cases). The counts are stored for eachchromosome or segment of interest and each corresponding normalizingchromosome or segment.

In certain embodiments, the reference genome has one or more excludedregions that are part of a true biological genome but are not includedin the reference genome. Reads potentially aligning to these excludedregions are not counted. Examples of excluded regions include regions oflong repeated sequences, regions of similarity between X and Ychromosomes, etc.

In some embodiments, the method determines whether to count a tag morethan once when multiple reads align to the same site on a referencegenome or sequence. There may be occasions when two tags have the samesequence and therefore align to an identical site on a referencesequence. The method employed to count tags may under certaincircumstances exclude from the count identical tags deriving from thesame sequenced sample. If a disproportionate number of tags areidentical in a given sample, it suggests that there is a strong bias orother defect in the procedure. Therefore, in accordance with certainembodiments, the counting method does not count tags from a given samplethat are identical to tags from the sample that were previously counted.

Various criteria may be set for choosing when to disregard an identicaltag from a single sample. In certain embodiments, a defined percentageof the tags that are counted must be unique. If more tags than thisthreshold are not unique, they are disregarded. For example, if thedefined percentage requires that at least 50% are unique, identical tagsare not counted until the percentage of unique tags it exceeds 50% forthe sample. In other embodiments, the threshold number of unique tags isat least about 60%. In other embodiments, the threshold percentage ofunique tags is at least about 75%, or at least about 90%, or at leastabout 95%, or at least about 98%, or at least about 99%. A threshold maybe set at 90% for chromosome 21. If 30M tags are aligned to chromosome21, then at least 27M of them must be unique. If 3M counted tags are notunique and the 30 million and first tag is not unique, it is notcounted. The choice of the particular threshold or other criterion usedto determine when not to count further identical tags can be selectedusing appropriate statistical analysis. One factor influencing thisthreshold or other criterion is the relative amount of sequenced sampleto the size of the genome to which tags can be aligned. Other factorsinclude the size of the reads and similar considerations.

In one embodiment, the number of test sequence tags mapped to a sequenceof interest is normalized to the known length of a sequence of interestto which they are mapped to provide a test sequence tag density ratio.As described for the qualified samples, normalization to the knownlength of a sequence of interest is not required, and may be included asa step to reduce the number of digits in a number to simplify it forhuman interpretation. As all the mapped test sequence tags are countedin the test sample, the sequence tag density for a sequence of intereste.g. a clinically-relevant sequence, in the test samples is determined,as are the sequence tag densities for additional sequences thatcorrespond to at least one normalizing sequence identified in thequalified samples.

In step 150, based on the identity of at least one normalizing sequencein the qualified samples, a test sequence dose is determined for asequence of interest in the test sample. In various embodiments, thetest sequence dose is computationally determined using by manipulatingthe sequence tag densities of the sequence of interest and thecorresponding normalizing sequence as described herein. Thecomputational apparatus responsible for this undertaking willelectronically access the association between the sequence of interestits associated normalizing sequence, which may be stored in a database,table, graph, or be included as code in program instructions.

As described elsewhere herein, the at least one normalizing sequence canbe a single sequence or a group of sequences. The sequence dose for asequence of interest in a test sample is a ratio of the sequence tagdensity determined for the sequence of interest in the test sample andthe sequence tag density of at least one normalizing sequence determinedin the test sample, wherein the normalizing sequence in the test samplecorresponds to the normalizing sequence identified in the qualifiedsamples for the particular sequence of interest. For example, if thenormalizing sequence identified for chromosome 21 in the qualifiedsamples is determined to be a chromosome e.g. chromosome 14, then thetest sequence dose for chromosome 21 (sequence of interest) isdetermined as the ratio of the sequence tag density for chromosome 21 inand the sequence tag density for chromosome 14 each determined in thetest sample. Similarly, chromosome doses for chromosomes 13, 18, X, Y,and other chromosomes associated with chromosomal aneuploidies aredetermined. A normalizing sequence for a chromosome of interest can beone or a group of chromosomes, or one or a group of chromosome segments.As described previously, a sequence of interest can be part of achromosome e.g. a chromosome segment. Accordingly, the dose for achromosome segment can be determined as the ratio of the sequence tagdensity determined for the segment in the test sample and the sequencetag density for the normalizing chromosome segment in the test sample,wherein the normalizing segment in the test sample corresponds to thenormalizing segment (single or a group of segments) identified in thequalified samples for the particular segment of interest. Chromosomesegments can range from kilobases (kb) to megabases (Mb) in size (e.g.,about 1 kb to 10 kb, or about 10 kb to 100 kb, or about 100 kb to 1 Mb).

In step 155, threshold values are derived from standard deviation valuesestablished for qualified sequence doses determined in a plurality ofqualified samples and sequence doses determined for samples known to beaneuploid for a sequence of interest. Note that this operation istypically performed asynchronously with analysis of patient testsamples. It may be performed, for example, concurrently with theselection of normalizing sequences from qualified samples. Accurateclassification depends on the differences between probabilitydistributions for the different classes i.e. type of aneuploidy. In someexamples, thresholds are chosen from empirical distribution for eachtype of aneuploidy e.g. trisomy 21. Possible threshold values that wereestablished for classifying trisomy 13, trisomy 18, trisomy 21, andmonosomy X aneuploidies as described in the Examples, which describe theuse of the method for determining chromosomal aneuploidies by sequencingcfDNA extracted from a maternal sample comprising a mixture of fetal andmaternal nucleic acids. The threshold value that is determined todistinguish samples affected for an aneuploidy of a chromosome can bethe same or can be different from the threshold that is determined todistinguish samples affected for a different aneuploidy. As is shown inthe Examples, the threshold value for each chromosome of interest isdetermined from the variability in the dose of the chromosome ofinterest across samples and sequencing runs. The less variable thechromosome dose for any chromosome of interest, the narrower the spreadin the dose for the chromosome of interest across all the unaffectedsamples, which are used to set the threshold for determining differentaneuploidies.

Returning to the process flow associated with classifying a patient testsample, in step 160, the copy number variation of the sequence ofinterest is determined in the test sample by comparing the test sequencedose for the sequence of interest to at least one threshold valueestablished from the qualified sequence doses. This operation may beperformed by the same computational apparatus employed to measuresequence tag densities and/or calculate segment doses.

In step 165, the calculated dose for a test sequence of interest iscompared to that set as the threshold values that are chosen accordingto a user-defined “threshold of reliability” to classify the sample as a“normal” an “affected” or a “no call”. The “no call” samples are samplesfor which a definitive diagnosis cannot be made with reliability. Eachtype of affected sample (e.g., trisomy 21, partial trisomy 21, monosomyX) has its own thresholds, one for calling normal (unaffected) samplesand another for calling affected samples (although in some cases the twothresholds coincide). As described elsewhere herein, under somecircumstances a no-call can be converted to a call (affected or normal)if fetal fraction of nucleic acid in the test sample is sufficientlyhigh. The classification of the test sequence may be reported by thecomputational apparatus employed in other operations of this processflow. In some cases, the classification is reported in an electronicformat and may be displayed, emailed, texted, etc. to interest persons.

Certain embodiments provide a method for providing prenatal diagnosis ofa fetal chromosomal aneuploidy in a biological sample comprising fetaland maternal nucleic acid molecules. The diagnosis is made based onobtaining sequence information sequencing at least a portion of themixture of the fetal and maternal nucleic acid molecules derived from abiological test sample e.g. a maternal plasma sample, computing from thesequencing data a normalizing chromosome dose for one or morechromosomes of interest, and/or a normalizing segment dose for one ormore segments of interest, and determining a statistically significantdifference between the chromosome dose for the chromosome of interestand/or the segment dose for the segment of interest, respectively, inthe test sample and a threshold value established in a plurality ofqualified (normal) samples, and providing the prenatal diagnosis basedon the statistical difference. As described in step 165 of the method, adiagnosis of normal or affected is made. A “no call” is provided in theevent that the diagnosis for normal or affected cannot be made withconfidence.

Samples and Sample Processing

Samples

Samples that are used for determining a CNV, e.g. chromosomalaneuploidies, partial aneuploidies, and the like, can include samplestaken from any cell, tissue, or organ in which copy number variationsfor one or more sequences of interest are to be determined. Desirably,the samples contain nucleic acids that are that are present in cellsand/or nucleic acids that are “cell-free” (e.g., cfDNA).

In some embodiments it is advantageous to obtain cell-free nucleic acidse.g. cell-free DNA (cfDNA). Cell-free nucleic acids, including cell-freeDNA, can be obtained by various methods known in the art from biologicalsamples including but not limited to plasma, serum, and urine (see,e.g., Fan et al., Proc Natl Acad Sci 105:16266-16271 [2008]; Koide etal., Prenatal Diagnosis 25:604-607 [2005]; Chen et al., Nature Med. 2:1033-1035 [1996]; Lo et al., Lancet 350: 485-487 [1997]; Botezatu etal., Clin Chem. 46: 1078-1084, 2000; and Su et al., J Mol. Diagn. 6:101-107 [2004]). To separate cell-free DNA from cells in a sample,various methods including, but not limited to fractionation,centrifugation (e.g., density gradient centrifugation), DNA-specificprecipitation, or high-throughput cell sorting and/or other separationmethods can be used. Commercially available kits for manual andautomated separation of cfDNA are available (Roche Diagnostics,Indianapolis, Ind., Qiagen, Valencia, Calif., Macherey-Nagel, Duren,Del.). Biological samples comprising cfDNA have been used in assays todetermine the presence or absence of chromosomal abnormalities e.g.trisomy 21, by sequencing assays that can detect chromosomalaneuploidies and/or various polymorphisms.

In various embodiments the cfDNA present in the sample can be enrichedspecifically or non-specifically prior to use (e.g., prior to preparinga sequencing library). Non-specific enrichment of sample DNA refers tothe whole genome amplification of the genomic DNA fragments of thesample that can be used to increase the level of the sample DNA prior topreparing a cfDNA sequencing library. Non-specific enrichment can be theselective enrichment of one of the two genomes present in a sample thatcomprises more than one genome. For example, non-specific enrichment canbe selective of the fetal genome in a maternal sample, which can beobtained by known methods to increase the relative proportion of fetalto maternal DNA in a sample. Alternatively, non-specific enrichment canbe the non-selective amplification of both genomes present in thesample. For example, non-specific amplification can be of fetal andmaternal DNA in a sample comprising a mixture of DNA from the fetal andmaternal genomes. Methods for whole genome amplification are known inthe art. Degenerate oligonucleotide-primed PCR (DOP), primer extensionPCR technique (PEP) and multiple displacement amplification (MDA) areexamples of whole genome amplification methods. In some embodiments, thesample comprising the mixture of cfDNA from different genomes isunenriched for cfDNA of the genomes present in the mixture. In otherembodiments, the sample comprising the mixture of cfDNA from differentgenomes is non-specifically enriched for any one of the genomes presentin the sample.

The sample comprising the nucleic acid(s) to which the methods describedherein are applied typically comprises a biological sample (“testsample”), e.g., as described above. In some embodiments, the nucleicacid(s) to be screened for one or more CNVs is purified or isolated byany of a number of well-known methods.

Accordingly, in certain embodiments the sample comprises or consists ofa purified or isolated polynucleotide, or it can comprise samples suchas a tissue sample, a biological fluid sample, a cell sample, and thelike. Suitable biological fluid samples include, but are not limited toblood, plasma, serum, sweat, tears, sputum, urine, sputum, ear flow,lymph, saliva, cerebrospinal fluid, ravages, bone marrow suspension,vaginal flow, transcervical lavage, brain fluid, ascites, milk,secretions of the respiratory, intestinal and genitourinary tracts,amniotic fluid, milk, and leukophoresis samples. In some embodiments,the sample is a sample that is easily obtainable by non-invasiveprocedures e.g. blood, plasma, serum, sweat, tears, sputum, urine,sputum, ear flow, saliva or feces. In certain embodiments the sample isa peripheral blood sample, or the plasma and/or serum fractions of aperipheral blood sample. In other embodiments, the biological sample isa swab or smear, a biopsy specimen, or a cell culture. In anotherembodiment, the sample is a mixture of two or more biological samplese.g. a biological sample can comprise two or more of a biological fluidsample, a tissue sample, and a cell culture sample. As used herein, theterms “blood,” “plasma” and “serum” expressly encompass fractions orprocessed portions thereof. Similarly, where a sample is taken from abiopsy, swab, smear, etc., the “sample” expressly encompasses aprocessed fraction or portion derived from the biopsy, swab, smear, etc.

In certain embodiments, samples can be obtained from sources, including,but not limited to, samples from different individuals, samples fromdifferent developmental stages of the same or different individuals,samples from different diseased individuals (e.g., individuals withcancer or suspected of having a genetic disorder), normal individuals,samples obtained at different stages of a disease in an individual,samples obtained from an individual subjected to different treatmentsfor a disease, samples from individuals subjected to differentenvironmental factors, samples from individuals with predisposition to apathology, samples individuals with exposure to an infectious diseaseagent (e.g., HIV), and the like.

In one illustrative, but non-limiting embodiment, the sample is amaternal sample that is obtained from a pregnant female, for example apregnant woman. In this instance, the sample can be analyzed using themethods described herein to provide a prenatal diagnosis of potentialchromosomal abnormalities in the fetus. The maternal sample can be atissue sample, a biological fluid sample, or a cell sample. A biologicalfluid includes, as non-limiting examples, blood, plasma, serum, sweat,tears, sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinalfluid, ravages, bone marrow suspension, vaginal flow, transcervicallavage, brain fluid, ascites, milk, secretions of the respiratory,intestinal and genitourinary tracts, and leukophoresis samples.

In another illustrative, but non-limiting embodiment, the maternalsample is a mixture of two or more biological samples e.g. thebiological sample can comprise two or more of a biological fluid sample,a tissue sample, and a cell culture sample. In some embodiments, thesample is a sample that is easily obtainable by non-invasive procedurese.g. blood, plasma, serum, sweat, tears, sputum, urine, milk, sputum,ear flow, saliva and feces. In some embodiments, the biological sampleis a peripheral blood sample, and/or the plasma and serum fractionsthereof. In other embodiments, the biological sample is a swab or smear,a biopsy specimen, or a sample of a cell culture. As disclosed above,the terms “blood,” “plasma” and “serum” expressly encompass fractions orprocessed portions thereof. Similarly, where a sample is taken from abiopsy, swab, smear, etc., the “sample” expressly encompasses aprocessed fraction or portion derived from the biopsy, swab, smear, etc.

In certain embodiments samples can also be obtained from in vitrocultured tissues, cells, or other polynucleotide-containing sources. Thecultured samples can be taken from sources including, but not limitedto, cultures (e.g., tissue or cells) maintained in different media andconditions (e.g., pH, pressure, or temperature), cultures (e.g., tissueor cells) maintained for different periods of length, cultures (e.g.,tissue or cells) treated with different factors or reagents (e.g., adrug candidate, or a modulator), or cultures of different types oftissue and/or cells.

Methods of isolating nucleic acids from biological sources are wellknown and will differ depending upon the nature of the source. One ofskill in the art can readily isolate nucleic acid(s) from a source asneeded for the method described herein. In some instances, it can beadvantageous to fragment the nucleic acid molecules in the nucleic acidsample. Fragmentation can be random, or it can be specific, as achieved,for example, using restriction endonuclease digestion. Methods forrandom fragmentation are well known in the art, and include, forexample, limited DNAse digestion, alkali treatment and physicalshearing. In one embodiment, sample nucleic acids are obtained from ascfDNA, which is not subjected to fragmentation.

In other illustrative embodiments, the sample nucleic acid(s) areobtained as genomic DNA, which is subjected to fragmentation intofragments of approximately 300 or more, approximately 400 or more, orapproximately 500 or more base pairs, and to which NGS methods can bereadily applied.

Sequencing Library Preparation

In one embodiment, the methods described herein can utilize nextgeneration sequencing technologies (NGS), that allow multiple samples tobe sequenced individually as genomic molecules (i.e. singleplexsequencing) or as pooled samples comprising indexed genomic molecules(e.g., multiplex sequencing) on a single sequencing run. These methodscan generate up to several hundred million reads of DNA sequences. Invarious embodiments the sequences of genomic nucleic acids, and/or ofindexed genomic nucleic acids can be determined using, for example, theNext Generation Sequencing Technologies (NGS) described herein. Invarious embodiments analysis of the massive amount of sequence dataobtained using NGS can be performed using one or more processors asdescribed herein.

In various embodiments the use of such sequencing technologies does notinvolve the preparation of sequencing libraries.

However, in certain embodiments the sequencing methods contemplatedherein involve the preparation of sequencing libraries. In oneillustrative approach, sequencing library preparation involves theproduction of a random collection of adapter-modified DNA fragments(e.g., polynucleotides) that are ready to be sequenced. Sequencinglibraries of polynucleotides can be prepared from DNA or RNA, includingequivalents, analogs of either DNA or cDNA, for example, DNA or cDNAthat is complementary or copy DNA produced from an RNA template, by theaction of reverse transcriptase. The polynucleotides may originate indouble-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA,PCR amplification products, and the like) or, in certain embodiments,the polynucleotides may originated in single-stranded form (e.g., ssDNA,RNA, etc.) and have been converted to dsDNA form. By way ofillustration, in certain embodiments, single stranded mRNA molecules maybe copied into double-stranded cDNAs suitable for use in preparing asequencing library. The precise sequence of the primary polynucleotidemolecules is generally not material to the method of librarypreparation, and may be known or unknown. In one embodiment, thepolynucleotide molecules are DNA molecules. More particularly, incertain embodiments, the polynucleotide molecules represent the entiregenetic complement of an organism or substantially the entire geneticcomplement of an organism, and are genomic DNA molecules (e.g., cellularDNA, cell free DNA (cfDNA), etc.), that typically include both intronsequence and exon sequence (coding sequence), as well as non-codingregulatory sequences such as promoter and enhancer sequences. In certainembodiments, the primary polynucleotide molecules comprise human genomicDNA molecules, e.g. cfDNA molecules present in peripheral blood of apregnant subject.

Preparation of sequencing libraries for some NGS sequencing platforms isfacilitated by the use of polynucleotides comprising a specific range offragment sizes. Preparation of such libraries typically involves thefragmentation of large polynucleotides (e.g. cellular genomic DNA) toobtain polynucleotides in the desired size range.

Fragmentation can be achieved by any of a number of methods known tothose of skill in the art. For example, fragmentation can be achieved bymechanical means including, but not limited to nebulization, sonicationand hydroshear. However mechanical fragmentation typically cleaves theDNA backbone at C—O, P—O and C—C bonds resulting in a heterogeneous mixof blunt and 3′- and 5′-overhanging ends with broken C—O, P—O and/C—Cbonds (see, e.g., Alnemri and Liwack, J Biol. Chem 265:17323-17333[1990]; Richards and Boyer, J Mol Biol 11:327-240 [1965]) which may needto be repaired as they may lack the requisite 5′-phosphate for thesubsequent enzymatic reactions e.g. ligation of sequencing adaptors,that are required for preparing DNA for sequencing.

In contrast, cfDNA, typically exists as fragments of less than about 300base pairs and consequently, fragmentation is not typically necessaryfor generating a sequencing library using cfDNA samples.

Typically, whether polynucleotides are forcibly fragmented (e.g.,fragmented in vitro), or naturally exist as fragments, they areconverted to blunt-ended DNA having 5′-phosphates and 3′-hydroxyl.Standard protocols e.g. protocols for sequencing using, for example, theIllumina platform as described elsewhere herein, instruct users toend-repair sample DNA, to purify the end-repaired products prior todA-tailing, and to purify the dA-tailing products prior to theadaptor-ligating steps of the library preparation.

Various embodiments, of methods of sequence library preparationdescribed herein obviate the need to perform one or more of the stepstypically mandated by standard protocols to obtain a modified DNAproduct that can be sequenced by NGS. An abbreviated method (ABBmethod), a 1-step method, and a 2-step method are described below.Consecutive dA-tailing and adaptor ligation is herein referred to as the2-step process. Consecutive dA-tailing, adaptor ligating, and amplifyingis herein referred to as the 1-step method. In various embodiments theABB and 2-step methods can be performed in solution or on a solidsurface. In certain embodiments the 1-step method is performed on asolid surface.

A comparison of a standard method e.g. Illumina, to the abbreviatedmethod (ABB; Example 2), the 2-step and the 1-step method (Examples 3-6)for preparing DNA molecules for sequencing by NGS according toembodiments of the present invention is diagrammed in FIG. 2.

Abbreviated Preparation—ABB

In one embodiment, an abbreviated method (ABB method) for thepreparation of a sequ3ence library is provided that comprises theconsecutive steps of end-repairing, dA-tailing and adaptor-ligating(ABB). In embodiments for preparing sequencing libraries that do notrequire the dA-tailing step (see, e.g., protocols for sequencing usingRoche 454 and SOLID™3 platforms) the steps of end-repairing andadaptor-ligating can exclude the purification step of the end-repairedproducts prior to the adaptor-ligating.

The method of preparing sequencing libraries comprising the consecutivesteps of end-repairing, dA tailing and adaptor ligating is hereinreferred to as the abbreviated method (ABB), and was shown to generatesequencing libraries of unexpectedly improved quality while expeditingthe analysis of samples (see, e.g., Example 2). According to someembodiments of the method, the ABB method can be performed in solution,as exemplified herein. The ABB method can also be performed on a solidsurface by first end-repairing and dA-tailing the DNA in solution, andsubsequently binding it to a solid surface as is described elsewhereherein for the 1-step or 2-step preparation on a solid surface. Thethree enzymatic steps, including the step of ligating the adaptors tothe dA-tailed DNA, are performed in the absence of polyethylene glycol.Published protocols for performing ligation reactions, includingligating adaptors to DNA, instruct users to perform ligations in thepresence of polyethylene glycol. Applicants determined that the ligationof the adaptors to the dA-tailed DNA can be performed in the absence ofpolyethylene glycol.

In another embodiment, the preparation of the sequencing libraryeliminates the need for end-repairing the cfDNA prior to the dA-tailingstep. Applicants have determined that cfDNA, which does not require tobe fragmented, does not need be end-repaired, and the preparation of thecfDNA sequencing library according to embodiments of the presentinvention exclude the end-repair step and the purification steps tocombine enzymatic reactions and further streamline the preparation ofthe DNA to be sequenced. cfDNA exists as a mixture of blunt and 3′- and5′-overhanging ends that are generated in vivo by the action ofnucleases, which cleave cellular genomic DNA into cfDNA fragments havingtermini with a 5′-phosphate and a 3′-hydroxyl group. Elimination of theend-repairing step selects cfDNA molecules that naturally occur asblunt-ended molecules, and of cfDNA molecules naturally having 5′overhanging ends that are filled-in by the polymerase activity of theenzyme e.g. Klenow Exo−, that is used to attach one or moredeoxynucleotide to the 3′-OH as described below (dA-tailing).Elimination of the end-repair step of cfDNA selects against cfDNAmolecules that have a 3′-overhanging end (3′-OH). Surprisingly,exclusion of these 3′-OH cfDNA molecules from the sequencing librarydoes not affect the representation of genomic sequences in the library,demonstrating that the end-repair step of cfDNA molecules may beexcluded from the preparation of the sequencing library (see Examples).In addition to cfDNA, other types of unrepaired polynucleotides that canbe used for preparing sequencing libraries include DNA moleculesresulting from reverse transcription of RNA molecules e.g. mRNA, siRNA,sRNA, and unrepaired DNA molecules that are amplicons of DNA synthesizedfrom phosphorylated primers. When unphosphorylated primers are used, DNAthat is reverse transcribed from RNA, and/or DNA that is amplified fromDNA templates i.e. DNA amplicons, can also be phosphorylated subsequentto their synthesis by a polynucleotide kinase.

In another embodiment, unrepaired DNA is used for preparing a sequencinglibrary according to the 2-step method, wherein end-repair of the DNA isexcluded, and unrepaired DNA is subjected to the two consecutive stepsof d-A tailing and adaptor ligating (see FIG. 2). The 2-step method canbe performed in solution or on a solid surface. When performed insolution, the 2-step method comprises utilizing DNA obtained from abiological sample, excluding the step of end-repairing the DNA, andadding a single deoxynucleotide e.g. deoxyadenosine (A) to the 3′-endsof the polynucleotides in the sample of unrepaired DNA, for example, bythe activity of certain types of DNA polymerase such as Taq polymeraseor Klenow Exo− polymerase. dA-tailed products, which are compatible with‘T’ overhang present on the 3′ terminus of each duplex region ofcommercially available adaptors are ligated to the adaptors in asubsequent consecutive step. dA-tailing prevents self-ligation of bothof the blunt-ended polynucleotides to favor the formation of theadaptor-ligated sequences. Thus, in some embodiments, unrepaired cfDNAis subjected to the consecutive steps of dA-tailing andadaptor-ligating, wherein the dA-tailed DNA is prepared from unrepairedDNA, and is not subjected to a purification step following thedA-tailing reaction. Double-stranded adaptors can be ligated to bothends of the dA-tailed DNA. A set of adaptors having the same sequences,or a set of two different adaptors can be utilized. In variousembodiments, one or more different sets of same or different adaptorscan also be used. Adaptors can comprise index sequences to enablemultiplex sequencing of the library DNA. Ligation of adaptors to thedA-tailed DNA can, optionally, be performed in the absence ofpolyethylene glycol.

2-Step—Preparation in Solution

In various embodiments, when the 2-step process is performed insolution, the products of the adaptor ligation reaction can be purifiedto remove unligated adaptors, adaptors that may have ligated to oneanother. The purification can also select a size range of templates forcluster generation, that can, optionally, be preceded by anamplification e.g. a PCR amplification. The ligation products can bepurified by any of a number of methods including, but not limited to gelelectrophoresis, solid-phase reversible immobilization (SPRI), and thelike. In some embodiments, the purified adaptor-ligated DNA is subjectedto an amplification e.g. PCR amplification, prior to sequencing. Somesequencing platforms require that the library DNA is further subjectedto another amplification. For example, the Illumina platform requiresthat a cluster amplification of library DNA be performed as an integralpart of the sequencing according to the Illumina technology. In otherembodiments, the purified adaptor-ligated DNA is denatured and thesingle stranded DNA molecules are attached to the flow cell of thesequencer. Thus, in some embodiments, the method for preparing asequencing library in solution from unrepaired DNA for NGS sequencingcomprises obtaining DNA molecules from a sample; and performing theconsecutive steps of dA tailing and adaptor-ligating the unrepaired DNAmolecules obtained from the sample.

As indicated supra, in various embodiments, these methods of librarypreparation are incorporated into a method of determining copy numbervariations (CNVs) such as aneuploidies, and the like. Accordingly, inone illustrative embodiment, a method is provided for determining thepresence or absence of one or more fetal chromosomal aneuploidiescomprising: (a) obtaining a maternal sample comprising a mixture offetal and maternal cell-free DNA; (b) isolating the mixture of fetal andmaternal cfDNA from said sample; (c) preparing a sequencing library fromthe mixture of fetal and maternal cfDNA; wherein preparing the librarycomprises the consecutive steps of dA-tailing and adaptor ligating thecfDNA, and wherein preparing the library excludes end-repairing thecfDNA and the preparation is performed in solution; (d) massivelyparallel sequencing at least a portion of the sequencing library toobtain sequence information for the fetal and maternal cfDNA in thesample; (e) storing in a computer readable medium, at least temporarily,the sequence information; (f) using the stored sequence information tocomputationally identify a number of sequence tags for each of one ormore chromosomes of interest and for a normalizing sequence for each ofany one or more chromosome of interest; (g) computationally calculating,using the number of sequence tags for each of the one or morechromosomes of interest and the number of sequence tags for thenormalizing sequence for each of the one or more chromosomes ofinterest, a chromosome dose for each of the one or more chromosomes ofinterest; and (h) comparing the chromosome dose for each of the one ormore chromosomes of interest to a corresponding threshold value for eachof the one or more chromosomes of interest, and thereby determining thepresence or absence of the fetal chromosomal aneuploidy in the sample,wherein steps (e)-(h) are performed using one or more processors. Thismethod is exemplified in Examples 3 and 4.

2-Step and 1-Step—Solid Phase Preparation

In some embodiments, the sequencing library is prepared on a solidsurface according to the 2-step method described above for thepreparation of the library in solution. The preparation of thesequencing library on a solid surface according to the 2-step methodcomprises obtaining DNA molecules e.g. cfDNA, from a sample, andperforming the consecutive steps of dA-tailing and adaptor ligating,where the adaptor-ligating is performed on a solid surface. Repaired orunrepaired DNA can be used. In some embodiments, the adaptor-ligatedproduct is detached from the solid surface, purified, and amplifiedprior to sequencing. In other embodiments, the adaptor-ligated productis detached from the solid surface, purified, and not amplified prior tosequencing. In yet other embodiments, the adaptor-ligated product isamplified, detached form the solid surface, and purified. In someembodiments, the purified product is amplified. In other embodiments,the purified product is not amplified. The sequencing protocol caninclude an amplification e.g. cluster amplification. In variousembodiments the detached adaptor-ligated product is purified prior toamplification and/or sequencing.

In certain embodiments, the sequencing library is prepared on a solidsurface according to the 1-step method. In various embodiments thepreparation of the sequencing library on a solid surface according tothe 1-step method comprises obtaining DNA molecules e.g. cfDNA, from asample, and performing the consecutive steps of dA-tailing, adaptorligating, and amplifying, wherein the adaptor-ligating is performed on asolid surface. The adaptor-ligated product need not be detached prior topurification.

FIG. 3 depicts 2-step and 1-step methods for preparing a sequencinglibrary on a solid surface. Either repaired or unrepaired DNA can beused for preparing a sequencing library on a solid surface. In someembodiments, unrepaired DNA is used. Examples of unrepaired DNA that canbe used for preparing a sequencing library on a solid surface includewithout limitation cfDNA, DNA that has been reverse transcribed from RNAusing phosphorylated primers, DNA that has been amplified from DNAtemplate using phosphorylated primers i.e. phosphorylated DNA amplicons.Examples of repaired DNA that can be used for preparing a sequencinglibrary on a solid surface include without limitation cfDNA andfragmented genomic DNA that has been blunt-ended and phosphorylated i.e.repaired, phosphorylated DNA generated by reverse transcription of RNAe.g. mRNA, sRNA, siRNA. In some illustrative embodiments, unrepairedcfDNA obtained from a maternal sample is used for preparing thesequencing library.

Preparation of a sequencing library on a solid surface comprises coatingthe solid surface with a first partner of a two-part conjugate,modifying a first adaptor by attaching the second partner of the twopart conjugate to the adaptor, and immobilizing the adaptor on the solidsurface by the binding interaction of the first and second partners ofthe two-part conjugate. For example, preparation of sequencing librarieson a solid surface can comprise attaching a polypeptide, polynucleotideor small molecule to an end of a library adaptor, which polypeptide,polynucleotide or small molecule is capable of forming a conjugatecomplex with a polypeptide, a polynucleotide or small molecule that isimmobilized on a solid surface. Solid surfaces that can be used forimmobilizing polypeptides, polynucleotides or small molecules includewithout limitation plastic, paper, membranes, filters, chips, pins orglass slides, silica or polymer beads (e.g. polypropylene, polystyrene,polycarbonate), 2D or 3D molecular scaffolds, or any support forsolid-phase synthesis of polypeptides or polynucleotides.

Bonding between polypeptide-polypeptide, polypeptide-polynucleotide,polypeptide-small molecule, and polynucleotide-polynucleotide conjugatescan be covalent or noncovalent. Preferably, conjugate complexes arebound by noncovalent bonds. For example, conjugates that can be used inpreparing sequencing libraries on a solid surface include withoutlimitation streptavidin-biotin conjugates, antibody-antigen conjugates,and ligand-receptor conjugates. Examples of polypeptide-polynucleotideconjugates that can be used in preparing sequencing libraries on a solidsurface include without limitation DNA-binding protein-DNA conjugates.Examples of polynucleotide-polynucleotide conjugates that can be used inpreparing sequencing libraries on a solid surface include withoutlimitation oligodT-oligoA, and oligodT-oligodA. Examples ofpolypeptide-small molecule and polynucleotide-small molecule conjugatesinclude streptavidin-biotin.

According to embodiments (1-step and 2-step) of the solid surface methodas shown in FIG. 3, the solid surface of the vessel used for preparingthe sequencing library e.g. a polypropylene PCR tube or 96-well plate,is coated with a polypeptide e.g. streptavidin. The end of a first setof adaptors is modified by attaching a small molecule e.g. a biotinmolecule, and the biotinylated adaptors are bound to the streptavidin onthe solid surface (1). Subsequently, the unrepaired or the repaired DNAis ligated to the streptavidin-bound biotinylated adaptor, therebyimmobilizing it to the solid surface (2). The second set of adaptors isligated to the immobilized DNA (3).

2-Step—Preparation on Solid Phase

In one embodiment, the 2-step method is performed using unrepaired DNAe.g. cfDNA, for preparing the sequencing library on a solid surface. Theunrepaired DNA is dA-tailed by attaching a single nucleotide base e.g.dA, to the 3′ ends of the unrepaired DNA e.g. cfDNA, strands.Optionally, multiple nucleotide bases can be attached to the unrepairedDNA. The mixture comprising the dA-tailed DNA is added to the adaptorsimmobilized on the solid surface, to which it is ligated. The steps ofdA-tailing and adaptor-ligating the DNA are consecutive i.e.purification of the dA-tailed product is not performed (as shown in FIG.2 for the 2-step method). As described above, the adaptors may haveoverhangs that are complementary to overhangs on the unrepaired DNAmolecule. Subsequently, a second set of adaptors is added to theDNA-biotinylated adaptor complex to provide an adaptor-ligated DNAlibrary. Optionally, repaired DNA is used for preparing the library.Repaired DNA can be genomic DNA that has been fragmented and subjectedto in vitro enzymatic repair of 3′ and 5′ ends. In one embodiment, DNAe.g. maternal cfDNA, is end-repaired, dA-tailed and adaptor-ligated toadaptors immobilized on a solid surface in consecutive steps ofend-repairing, dA-tailing and adaptor-ligating as described for theabbreviated method performed in solution.

In certain embodiments utilizing the 2-step process, the adaptor-ligatedDNA is detached from the solid surface by chemical or physical meanse.g. heat, UV light etc. (4 a in FIG. 2), is purified (5 in FIG. 2), andoptionally, it is subjected to an amplification in solution prior tobeginning the sequencing process. In other embodiments, theadaptor-ligated DNA is not amplified. Absent amplification, the adaptorsligated to the DNA can be constructed to comprise sequences thathybridize to oligonucleotides present on the flow cell of a sequencer(Kozarewa et al., Nat Methods 6:291-295 [2009]), and an amplificationthat introduces sequences for hybridizing the library DNA to the flowcell of a sequencer is avoided. The library of adaptor-ligated DNA issubjected to massively parallel sequencing (6 in FIG. 2) as describedfor the adaptor-ligated DNA created in solution. In some embodiments,sequencing is massively parallel sequencing usingsequencing-by-synthesis with reversible dye terminators. In otherembodiments, sequencing is massively parallel sequencing usingsequencing-by-ligation. The sequencing process may include a solid-phaseamplification e.g. cluster amplification, as described elsewhere herein.

Thus, in various embodiments, the method for preparing a sequencinglibrary on a solid surface from unrepaired DNA for NGS can compriseobtaining DNA molecules from a sample; and performing the consecutivesteps of dA tailing and adaptor-ligating the unrepaired DNA molecules,where adaptor-ligating is performed on a solid phase. In certainembodiments, the adaptors can include index sequences, to allow formultiplexing the sequencing of multiple samples within a single reactionvessel e.g. a channel of a flow cell. As described above, the DNAmolecules can be cfDNA molecules, they can be DNA molecules transcribedfrom RNA, they can be amplicons of DNA molecules, and the like.

As indicated supra, in various embodiments, these methods of librarypreparation are incorporated into a method of determining copy numbervariations (CNVs) such as aneuploidies, and the like. Thus, in someembodiments the method for preparing a sequencing library on a solidsurface from unrepaired cfDNA is incorporated into a method foranalyzing a maternal sample to determine the presence or absence of afetal chromosomal aneuploidy. Accordingly, in one embodiment, a methodis provided for determining the presence or absence of one or more fetalchromosomal aneuploidies comprising: (a) obtaining a maternal samplecomprising a mixture of fetal and maternal cell-free DNA; (b) isolatingthe mixture of fetal and maternal cfDNA from said sample; (c) preparinga sequencing library from the mixture of fetal and maternal cfDNA;wherein preparing the library comprises the consecutive steps ofdA-tailing and adaptor ligating the cfDNA, where preparing the libraryexcludes end-repairing the cfDNA and the preparation is performed on asolid surface; (d) massively parallel sequencing at least a portion ofthe sequencing library to obtain sequence information for the fetal andmaternal cfDNA in the sample; (e) storing in a computer readable medium,at least temporarily, the sequence information; (f) using the storedsequence information to computationally identify a number of sequencetags for each of one or more chromosomes of interest and for anormalizing sequence for each of any one or more chromosome of interest;(g) computationally calculating, using the number of sequence tags foreach of the one or more chromosomes of interest and the number ofsequence tags for the normalizing sequence for each of the one or morechromosomes of interest, a chromosome dose for each of the one or morechromosomes of interest; and (h) comparing the chromosome dose for eachof the one or more chromosomes of interest to a corresponding thresholdvalue for each of the one or more chromosomes of interest, and therebydetermining the presence or absence of the fetal chromosomal aneuploidyin the sample, wherein steps (e)-(h) are performed using one or moreprocessors. The sample can be a biological fluid sample e.g. plasma,serum, urine and saliva. In some embodiments, the sample is a maternalblood sample, or the plasma or serum fraction thereof. This method isexemplified in Example 4.

1-Step—Preparation on Solid Phase

In another embodiment, unrepaired DNA is dA-tailed, but the dA-tailedproduct is not purified prior to amplification such that the steps ofdA-tailing, adaptor-ligating and amplifying are performed consecutivelyor sequentially. Consecutive dA-tailing, adaptor ligating and amplifyingfollowed by purification prior to sequencing, is herein referred to asthe 1-step process. The 1-step method can be performed on a solidsurface (see, e.g., FIG. 3). The steps of attaching the first set ofadaptors to a solid surface (1), ligating unrepaired and dA-tailed DNAto the surface-bound adaptors (2), and ligating the second set ofadaptors to the surface-bound DNA (3), can be performed as described forthe 2-step method above. In the 1-step method, however, theadaptor-ligated surface-bound DNA can be amplified while attached to thesolid surface (4 b in FIG. 2). Subsequently, the resulting library ofadaptor-ligated DNA created on a solid surface is detached and purified(5 in FIG. 2) prior to being subjected to massively parallel sequencingas described for the adaptor-ligated DNA created in solution. In someembodiments, sequencing is massively parallel sequencing usingsequencing-by-synthesis with reversible dye terminators. In otherembodiments, sequencing is massively parallel sequencing usingsequencing-by-ligation.

Accordingly, in some embodiments, the a method is provided for preparinga sequencing library for NGS sequencing, by performing the stepscomprising obtaining DNA molecules from a sample; and performing theconsecutive steps of dA-tailing, adaptor-ligating, and amplifying theDNA molecules, where the adaptor-ligating is performed on a solidsurface. As described for the 2-step method, in various embodiments, theadaptors can include index sequences to allow for multiplexing thesequencing of multiple samples within a single reaction vessel e.g. achannel of a flow cell.

In some embodiments, the DNA can be repaired. The DNA molecules can becfDNA molecules, they can be DNA molecules transcribed from RNA, or theDNA molecules can be amplicons of DNA molecules. Adaptor-ligation isperformed as described above. Excess unligated adaptors can be washedfrom the immobilized adaptor-ligated DNA; reagents required for anamplification are added to the immobilized adaptor-ligated DNA, which issubjected to cycles of amplification e.g. PCR amplification, as is knownin the art. In other embodiments, the adaptor-ligated DNA is notamplified. Absent amplification the adaptor-ligated DNA can be removedfrom the solid surface by chemical or physical means e.g. heat, UV lightetc. Absent amplification, the adaptors ligated to the DNA can comprisesequences that hybridize to oligonucleotides present on the flow cell ofthe sequencer (Kozarewa et al., Nat Methods 6:291-295 [2009]).

In various embodiments the sample can be a biological fluid sample(e.g., blood, plasma, serum, urine, cerebrospinal fluid, amniotic fluid,saliva, and the like). In some embodiments the method for preparing asequencing library on a solid surface from unrepaired cfDNA is includedas a step in a method for analyzing a maternal sample to determine thepresence or absence of a fetal chromosomal aneuploidy.

Accordingly, in one embodiment, a method is provided for determining thepresence or absence of one or more fetal chromosomal aneuploidiescomprising: (a) obtaining a maternal sample comprising a mixture offetal and maternal cell-free DNA; (b) isolating the mixture of fetal andmaternal cfDNA from said sample; (c) preparing a sequencing library fromthe mixture of fetal and maternal cfDNA; wherein preparing the librarycomprises the consecutive steps of dA-tailing, adaptor ligating, andamplifying the cfDNA, and wherein the preparation is performed on asolid surface; (d) massively parallel sequencing at least a portion ofthe sequencing library to obtain sequence information for the fetal andmaternal cfDNA in the sample; (e) storing in a computer readable medium,at least temporarily, the sequence information; (f) using the storedsequence information to computationally identify a number of sequencetags for each of one or more chromosomes of interest and for anormalizing sequence for each of any one or more chromosome of interest;(g) computationally calculating, using the number of sequence tags foreach of the one or more chromosomes of interest and the number ofsequence tags for the normalizing sequence for each of the one or morechromosomes of interest, a chromosome dose for each of the one or morechromosomes of interest; and (h) comparing the chromosome dose for eachof the one or more chromosomes of interest to a corresponding thresholdvalue for each of the one or more chromosomes of interest, and therebydetermining the presence or absence of the fetal chromosomal aneuploidyin the sample, wherein steps (e)-(h) are performed using one or moreprocessors. In some embodiments, the DNA is end-repaired. In otherembodiments, preparing the library excludes end-repairing the cfDNA.This method is exemplified in Examples 5 and 6.

The processes for preparing sequencing libraries as described above areapplicable to methods of sample analyses including without limitationmethods for determining copy number variations (CNV), and methods fordetermining the presence or absence of polymorphisms of any sequence ofinterest in samples containing single genomes and in samples containingmixtures of at least two genomes, which are known or are suspected todiffer in one or more sequence of interest.

An amplification of the adaptor-ligated product prepared on a solidphase or in solution may be required to introduce to the adaptor ligatedtemplate molecules the oligonucleotide sequences that are required forhybridization to the flow cell or other surface present in some of theNGS platforms. The contents of an amplification reaction are known byone skilled in the art and include appropriate substrates (such asdNTPs), enzymes (e.g. a DNA polymerase) and buffer components requiredfor an amplification reaction. Optionally, amplification ofadaptor-ligated polynucleotides can be omitted. Generally amplificationreactions require at least two amplification primers e.g. primeroligonucleotides, that can be identical or different and that caninclude an “adaptor-specific portion” capable of annealing to aprimer-binding sequence in the polynucleotide molecule to be amplified(or the complement thereof if the template is viewed as a single strand)during the annealing step.

Once formed, the library of templates prepared according to the methodsdescribed above can be used for solid-phase nucleic acid amplificationthat may be required by some NGS platforms. The term “solid-phaseamplification” as used herein refers to any nucleic acid amplificationreaction carried out on or in association with a solid support such thatall or a portion of the amplified products are immobilized on the solidsupport as they are formed. In particular embodiments, the termencompasses solid-phase polymerase chain reaction (solid-phase PCR) andsolid phase isothermal amplification which are reactions analogous tostandard solution phase amplification, except that one or both of theforward and reverse amplification primers is/are immobilized on thesolid support. Solid phase PCR also includes systems such as emulsions,where one primer is anchored to a bead and the other is in freesolution, and colony formation in solid phase gel matrices wherein oneprimer is anchored to the surface, and one is in free solution.

In various embodiments following amplification, and sequencing librariescan be analyzed by microfluidic capillary electrophoresis to ensure thatthe library is free of adaptor dimers or single stranded DNA. Thelibrary of template polynucleotide molecules is particularly suitablefor use in solid phase sequencing methods. In addition to providingtemplates for solid-phase sequencing and solid-phase PCR, librarytemplates provide templates for whole genome amplification.

Marker Nucleic Acids for Tracking and Verifying Sample Integrity

In various embodiments verification of the integrity of the samples andsample tracking can be accomplished by sequencing mixtures of samplegenomic nucleic acids e.g. cfDNA, and accompanying marker nucleic acidsthat have been introduced into the samples, e.g., prior to processing.

Marker nucleic acids can be combined with the test sample (e.g.,biological source sample) and subjected to processes that include, forexample, one or more of the steps of fractionating the biological sourcesample e.g. obtaining an essentially cell-free plasma fraction from awhole blood sample, purifying nucleic acids from a fractionated e.g.plasma, or unfractionated biological source sample e.g. a tissue sample,and sequencing. In some embodiments, sequencing comprises preparing asequencing library. The sequence or combination of sequences of themarker molecules that are combined with a source sample is chosen to beunique to the source sample. In some embodiments, the unique markermolecules in a sample all have the same sequence. In other embodiments,the unique marker molecules in a sample are a plurality of sequences,e.g., a combination of two, three, four, five, six, seven, eight, nine,ten, fifteen, twenty, or more different sequences.

In one embodiment, the integrity of a sample can be verified using aplurality of marker nucleic acid molecules having identical sequences.Alternatively, the identity of a sample can be verified using aplurality of marker nucleic acid molecules that have at least two, atleast three, at least four, at least five, at least six, at least seven,at least eight, at least nine, at least ten, at least 11, at least 12,at least 13, at least 13, at least 14, at least 15, at least 16, atleast 17m, at least 18, at least 19, at least 20, at least 25, at least30, at least 35, at least 40, at least 50, or more different sequences.Verification of the integrity of the plurality of biological samplesi.e. two or more biological samples, requires that each of the two ormore samples be marked with marker nucleic acids that have sequencesthat are unique to each of the plurality of test sample that is beingmarked. For example, a first sample can be marked with a marker nucleicacid having sequence A, and a second sample can be marked with a markernucleic acid having sequence B. Alternatively, a first sample can bemarked with marker nucleic acid molecules all having sequence A, and asecond sample can be marked with a mixture of sequences B and C, whereinsequences A, B and C are marker molecules having different sequences.

The marker nucleic acid(s) can be added to the sample at any stage ofsample preparation that occurs prior to library preparation (iflibraries are to be prepared) and sequencing. In one embodiment, markermolecules can be combined with an unprocessed source sample. Forexample, the marker nucleic acid can be provided in a collection tubethat is used to collect a blood sample. Alternatively, the markernucleic acids can be added to the blood sample following the blood draw.In one embodiment, the marker nucleic acid is added to the vessel thatis used to collect a biological fluid sample e.g. the marker nucleicacid(s) are added to a blood collection tube that is used to collect ablood sample. In another embodiment, the marker nucleic acid(s) areadded to a fraction of the biological fluid sample. For example, themarker nucleic acid is added to the plasma and/or serum fraction of ablood sample e.g. a maternal plasma sample. In yet another embodiment,the marker molecules are added to a purified sample e.g. a sample ofnucleic acids that have been purified from a biological sample. Forexample, the marker nucleic acid is added to a sample of purifiedmaternal and fetal cfDNA. Similarly, the marker nucleic acids can beadded to a biopsy specimen prior to processing the specimen. In someembodiments, the marker nucleic acids can be combined with a carrierthat delivers the marker molecules into the cells of the biologicalsample. Cell-delivery carriers include pH-sensitive and cationicliposomes.

In various embodiments, the marker molecules have antigenomic sequences,that are sequences that are absent from the genome of the biologicalsource sample. In an exemplary embodiment, the marker molecules that areused to verify the integrity of a human biological source sample havesequences that are absent from the human genome. In an alternativeembodiment, the marker molecules have sequences that are absent from thesource sample and from any one or more other known genomes. For example,the marker molecules that are used to verify the integrity of a humanbiological source sample have sequences that are absent from the humangenome and from the mouse genome. The alternative allows for verifyingthe integrity of a test sample that comprises two or more genomes. Forexample, the integrity of a human cell-free DNA sample obtained from asubject affected by a pathogen e.g. a bacterium, can be verified usingmarker molecules having sequences that are absent from both the humangenome and the genome of the affecting bacterium. Sequences of genomesof numerous pathogens e.g. bacteria, viruses, yeasts, fungi, protozoaetc., are publicly available on the world wide web atncbi.nlm.nih.gov/genomes. In another embodiment, marker molecules arenucleic acids that have sequences that are absent from any known genome.The sequences of marker molecules can be randomly generatedalgorithmically.

In various embodiments the marker molecules can be naturally-occurringdeoxyribonucleic acids (DNA), ribonucleic acids or artificial nucleicacid analogs (nucleic acid mimics) including peptide nucleic acids(PMA), morpholino nucleic acid, locked nucleic acids, glycol nucleicacids, and threose nucleic acids, which are distinguished fromnaturally-occurring DNA or RNA by changes to the backbone of themolecule or DNA mimics that do not have a phosphodiester backbone. Thedeoxyribonucleic acids can be from naturally-occurring genomes or can begenerated in a laboratory through the use of enzymes or by solid phasechemical synthesis. Chemical methods can also be used to generate theDNA mimics that are not found in nature. Derivatives of DNA are that areavailable in which the phosphodiester linkage has been replaced but inwhich the deoxyribose is retained include but are not limited to DNAmimics having backbones formed by thioformacetal or a carboxamidelinkage, which have been shown to be good structural DNA mimics. OtherDNA mimics include morpholino derivatives and the peptide nucleic acids(PNA), which contain an N-(2-aminoethyl)glycine-based pseudopeptidebackbone (Ann Rev Biophys Biomol Struct 24:167-183 [1995]). PNA is anextremely good structural mimic of DNA (or of ribonucleic acid [RNA]),and PNA oligomers are able to form very stable duplex structures withWatson-Crick complementary DNA and RNA (or PNA) oligomers, and they canalso bind to targets in duplex DNA by helix invasion (Mol Biotechnol26:233-248 [2004]. Another good structural mimic/analog of DNA analogthat can be used as a marker molecule is phosphorothioate DNA in whichone of the non-bridging oxygens is replaced by a sulfur. Thismodification reduces the action of endo- and exonucleases2 including 5′to 3′ and 3′ to 5′ DNA POL 1 exonuclease, nucleases Si and P1, RNases,serum nucleases and snake venom phosphodiesterase.

The length of the marker molecules can be distinct or indistinct fromthat of the sample nucleic acids i.e. the length of the marker moleculescan be similar to that of the sample genomic molecules, or it can begreater or smaller than that of the sample genomic molecules. The lengthof the marker molecules is measured by the number of nucleotide ornucleotide analog bases that constitute the marker molecule. Markermolecules having lengths that differ from those of the sample genomicmolecules can be distinguished from source nucleic acids usingseparation methods known in the art. For example, differences in thelength of the marker and sample nucleic acid molecules can be determinedby electrophoretic separation e.g. capillary electrophoresis. Sizedifferentiation can be advantageous for quantifying and assessing thequality of the marker and sample nucleic acids. Preferably, the markernucleic acids are shorter than the genomic nucleic acids, and ofsufficient length to exclude them from being mapped to the genome of thesample. For example, as a 30 base human sequence is needed to uniquelymap it to a human genome. Accordingly in certain embodiments, markermolecules used in sequencing bioassays of human samples should be atleast 30 bp in length.

The choice of length of the marker molecule is determined primarily bythe sequencing technology that is used to verify the integrity of asource sample. The length of the sample genomic nucleic acids beingsequenced can also be considered. For example, some sequencingtechnologies employ clonal amplification of polynucleotides, which canrequire that the genomic polynucleotides that are to be clonallyamplified be of a minimum length. For example, sequencing using theIllumina GAII sequence analyzer includes an in vitro clonalamplification by bridge PCR (also known as cluster amplification) ofpolynucleotides that have a minimum length of 110 bp, to which adaptorsare ligated to provide a nucleic acid of at least 200 bp and less than600 bp that can be clonally amplified and sequenced. In someembodiments, the length of the adaptor-ligated marker molecule isbetween about 200 bp and about 600 bp, between about 250 bp and 550 bp,between about 300 bp and 500 bp, or between about 350 and 450. In otherembodiments, the length of the adaptor-ligated marker molecule is about200 bp. For example, when sequencing fetal cfDNA that is present in amaternal sample, the length of the marker molecule can be chosen to besimilar to that of fetal cfDNA molecules. Thus, in one embodiment, thelength of the marker molecule used in an assay that comprises massivelyparallel sequencing of cfDNA in a maternal sample to determine thepresence or absence of a fetal chromosomal aneuploidy, can be about 150bp, about 160 bp, 170 bp, about 180 bp, about 190 bp or about 200 bp;preferably, the marker molecule is about 170 bp. Other sequencingapproaches e.g. SOLiD sequencing, Polony Sequencing and 454 sequencinguse emulsion PCR to clonally amplify DNA molecules for sequencing, andeach technology dictates the minimum and the maximum length of themolecules that are to be amplified. The length of marker molecules to besequenced as clonally amplified nucleic acids can be up to about 600 bp.In some embodiments, the length of marker molecules to be sequenced canbe greater than 600 bp.

Single molecule sequencing technologies, that do not employ clonalamplification of molecules, and are capable of sequencing nucleic acidsover a very broad range of template lengths, in most situations do notrequire that the molecules to be sequenced be of any specific length.However, the yield of sequences per unit mass is dependent on the numberof 3′ end hydroxyl groups, and thus having relatively short templatesfor sequencing is more efficient than having long templates. If startingwith nucleic acids longer than 1000 nt, it is generally advisable toshear the nucleic acids to an average length of 100 to 200 nt so thatmore sequence information can be generated from the same mass of nucleicacids. Thus, the length of the marker molecule can range from tens ofbases to thousands of bases. The length of marker molecules used forsingle molecule sequencing can be up to about 25 bp, up to about 50 bp,up to about 75 bp, up to about 100 bp, up to about 200 bp, up to about300 bp, up to about 400 bp, up to about 500 bp, up to about 600 bp, upto about 700 bp, up to about 800 bp, up to about 900 bp, up to about1000 bp, or more in length.

The length chosen for a marker molecule is also determined by the lengthof the genomic nucleic acid that is being sequenced. For example, cfDNAcirculates in the human bloodstream as genomic fragments of cellulargenomic DNA. Fetal cfDNA molecules found in the plasma of pregnant womenare generally shorter than maternal cfDNA molecules (Chan et al., ClinChem 50:8892 [2004]). Size fractionation of circulating fetal DNA hasconfirmed that the average length of circulating fetal DNA fragments is<300 bp, while maternal DNA has been estimated to be between about 0.5and 1 Kb (Li et al., Clin Chem, 50: 1002-1011 [2004]). These findingsare consistent with those of Fan et al., who determined using NGS thatfetal cfDNA is rarely >340 bp (Fan et al., Clin Chem 56:1279-1286[2010]). DNA isolated from urine with a standard silica-based methodconsists of two fractions, high molecular weight DNA, which originatesfrom shed cells and low molecular weight (150-250 base pair) fraction oftransrenal DNA (Tr-DNA) (Botezatu et al., Clin Chem. 46: 1078-1084,2000; and Su et al., J Mol. Diagn. 6: 101-107, 2004). The application ofnewly developed technique for isolation of cell-free nucleic acids frombody fluids to the isolation of transrenal nucleic acids has revealedthe presence in urine of DNA and RNA fragments much shorter than 150base pairs (U.S. Patent Application Publication No. 20080139801). Inembodiments, wherein cfDNA is the genomic nucleic acid that issequenced, marker molecules that are chosen can be up to about thelength of the cfDNA. For example, the length of marker molecules used inmaternal cfDNA samples to be sequenced as single nucleic acid moleculesor as clonally amplified nucleic acids can be between about 100 bp and600. In other embodiments, the sample genomic nucleic acids arefragments of larger molecules. For example, a sample genomic nucleicacid that is sequenced is fragmented cellular DNA. In embodiments, whenfragmented cellular DNA is sequenced, the length of the marker moleculescan be up to the length of the DNA fragments. In some embodiments, thelength of the marker molecules is at least the minimum length requiredfor mapping the sequence read uniquely to the appropriate referencegenome. In other embodiments, the length of the marker molecule is theminimum length that is required to exclude the marker molecule frombeing mapped to the sample reference genome.

In addition, marker molecules can be used to verify samples that are notassayed by nucleic acid sequencing, and that can be verified by commonbiotechniques other than sequencing e.g. real-time PCR.

Sample Controls (e.g., in Process Positive Controls for Sequencingand/or Analysis).

In various embodiments marker sequences introduced into the samples,e.g., as described above, can function as positive controls to veritythe verify the accuracy and efficacy of sequencing and subsequentprocessing and analysis.

Accordingly, compositions and method for providing an in-processpositive control (IPC) for sequencing DNA in a sample are provided. Incertain embodiments, positive controls are provided for sequencing cfDNAin a sample comprising a mixture of genomes are provided. An IPC can beused to relate baseline shifts in sequence information obtained fromdifferent sets of samples e.g. samples that are sequenced at differenttimes on different sequencing runs. Thus, for example, an IPC can relatethe sequence information obtained for a maternal test sample to thesequence information obtained from a set of qualified samples that weresequenced at a different time.

Similarly, in the case of segment analysis, an IPC can relate thesequence information obtained from a subject for particular segment(s)to the sequence obtained from a set of qualified samples (of similarsequences) that were sequenced at a different time. In certainembodiments an IPC can relate the sequence information obtained from asubject for particular cancer-related loci to the sequence informationobtained from a set of qualified samples (e.g., from a knownamplification/deletion, and the like).

In addition, IPCs can be used as markers to track sample(s) through thesequencing process. IPCs can also provide a qualitative positivesequence dose value e.g. NCV, for one or more aneuploidies ofchromosomes of interest e.g. trisomy 21, trisomy 13, trisomy 18 toprovide proper interpretation, and to ensure the dependability andaccuracy of the data. In certain embodiments IPCs can be created tocomprise nucleic acids from male and female genomes to provide doses forchromosomes X and Y in a maternal sample to determine whether the fetusis male.

The type and the number of in-process controls depends on the type ornature of the test needed. For example, for a test requiring thesequencing of DNA from a sample comprising a mixture of genomes todetermine whether a chromosomal aneuploidy exists, the in-processcontrol can comprise DNA obtained from a sample known comprising thesame chromosomal aneuploidy that is being tested. In some embodiments,the IPC includes DNA from a sample known to comprise an aneuploidy of achromosome of interest. For example, the IPC for a test to determine thepresence or absence of a fetal trisomy e.g. trisomy 21, in a maternalsample comprises DNA obtained from an individual with trisomy 21. Insome embodiments, the IPC comprises a mixture of DNA obtained from twoor more individuals with different aneuploidies. For example, for a testto determine the presence or absence of trisomy 13, trisomy 18, trisomy21, and monosomy X, the IPC comprises a combination of DNA samplesobtained from pregnant women each carrying a fetus with one of thetrisomies being tested. In addition to complete chromosomalaneuploidies, IPCs can be created to provide positive controls for teststo determine the presence or absence of partial aneuploidies.

An IPC that serves as the control for detecting a single aneuploidy canbe created using a mixture of cellular genomic DNA obtained from a twosubjects one being the contributor of the aneuploid genome. For example,an IPC that is created as a control for a test to determine a fetaltrisomy e.g. trisomy 21, can be created by combining genomic DNA from amale or female subject carrying the trisomic chromosome with genomic DNAwith a female subject known not to carry the trisomic chromosome.Genomic DNA can be extracted from cells of both subjects, and sheared toprovide fragments of between about 100-400 bp, between about 150-350 bp,or between about 200-300 bp to simulate the circulating cfDNA fragmentsin maternal samples. The proportion of fragmented DNA from the subjectcarrying the aneuploidy e.g. trisomy 21, is chosen to simulate theproportion of circulating fetal cfDNA found in maternal samples toprovide an IPC comprising a mixture of fragmented DNA comprising about5%, about 10%, about 15%, about 20%, about 25%, about 30%, of DNA fromthe subject carrying the aneuploidy. The IPC can comprise DNA fromdifferent subjects each carrying a different aneuploidy. For example,the IPC can comprise about 80% of the unaffected female DNA, and theremaining 20% can be DNA from three different subjects each carrying atrisomic chromosome 21, a trisomic chromosome 13, and a trisomicchromosome 18. The mixture of fragmented DNA is prepared for sequencing.Processing of the mixture of fragmented DNA can comprise preparing asequencing library, which can be sequenced using any massively parallelmethods in singleplex or multiplex fashion. Stock solutions of thegenomic IPC can be stored and used in multiple diagnostic tests.

Alternatively the IPC can be created using cfDNA obtained from a motherknown to carry a fetus with a known chromosomal aneuploidy. For example,cfDNA can be obtained from a pregnant woman carrying a fetus withtrisomy 21. The cfDNA is extracted from the maternal sample, and clonedinto a bacterial vector and grown in bacteria to provide an ongoingsource of the IPC. The DNA can be extracted from the bacterial vectorusing restriction enzymes. Alternatively, the cloned cfDNA can beamplified by e.g. PCR. The IPC DNA can be processed for sequencing inthe same runs as the cfDNA from the test samples that are to be analyzedfor the presence or absence of chromosomal aneuploidies.

While the creation of IPCs is described above with respect to trisomys,it will be appreciated that IPCs can be created to reflect other partialaneuploidies including for example, various segment amplification and/ordeletions. Thus, for example, where various cancers are known to beassociated with particular amplifications (e.g., breast cancerassociated with 20Q13) IPCs can be created that incorporate those knownamplifications.

Sequencing Methods

As indicated above, the prepared samples (e.g., Sequencign Libraries)are sequenced as part of the procedure for identifying copy numbervariation(s). Any of a number of sequencing technologies can beutilized.

Some sequencing technologies are available commercially, such as thesequencing-by-hybridization platform from Affymetrix Inc. (Sunnyvale,Calif.) and the sequencing-by-synthesis platforms from 454 Life Sciences(Bradford, Conn.), Illumina/Solexa (Hayward, Calif.) and HelicosBiosciences (Cambridge, Mass.), and the sequencing-by-ligation platformfrom Applied Biosystems (Foster City, Calif.), as described below. Inaddition to the single molecule sequencing performed usingsequencing-by-synthesis of Helicos Biosciences, other single moleculesequencing technologies include, but are not limited to, the SMRT™technology of Pacific Biosciences, the ION TORRENT™ technology, andnanopore sequencing developed for example, by Oxford NanoporeTechnologies.

While the automated Sanger method is considered as a ‘first generation’technology, Sanger sequencing including the automated Sanger sequencing,can also be employed in the methods described herein. Additionalsuitable sequencing methods include, but are not limited to nucleic acidimaging technologies e.g. atomic force microscopy (AFM) or transmissionelectron microscopy (TEM). Illustrative sequencing technologies aredescribed in greater detail below.

In one illustrative, but non-limiting, embodiment, the methods describedherein comprise obtaining sequence information for the nucleic acids ina test sample e.g. cfDNA in a maternal sample, cfDNA or cellular DNA ina subject being screened for a cancer, and the like, using singlemolecule sequencing technology of the Helicos True Single MoleculeSequencing (tSMS) technology (e.g. as described in Harris T. D. et al.,Science 320:106-109 [2008]). In the tSMS technique, a DNA sample iscleaved into strands of approximately 100 to 200 nucleotides, and apolyA sequence is added to the 3′ end of each DNA strand. Each strand islabeled by the addition of a fluorescently labeled adenosine nucleotide.The DNA strands are then hybridized to a flow cell, which containsmillions of oligo-T capture sites that are immobilized to the flow cellsurface. In certain embodiments the templates can be at a density ofabout 100 million templates/cm². The flow cell is then loaded into aninstrument, e.g., HeliScope™ sequencer, and a laser illuminates thesurface of the flow cell, revealing the position of each template. A CCDcamera can map the position of the templates on the flow cell surface.The template fluorescent label is then cleaved and washed away. Thesequencing reaction begins by introducing a DNA polymerase and afluorescently labeled nucleotide. The oligo-T nucleic acid serves as aprimer. The polymerase incorporates the labeled nucleotides to theprimer in a template directed manner. The polymerase and unincorporatednucleotides are removed. The templates that have directed incorporationof the fluorescently labeled nucleotide are discerned by imaging theflow cell surface. After imaging, a cleavage step removes thefluorescent label, and the process is repeated with other fluorescentlylabeled nucleotides until the desired read length is achieved. Sequenceinformation is collected with each nucleotide addition step. Wholegenome sequencing by single molecule sequencing technologies excludes ortypically obviates PCR-based amplification in the preparation of thesequencing libraries, and the methods allow for direct measurement ofthe sample, rather than measurement of copies of that sample.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample e.g. cfDNA in a maternal test sample, cfDNA orcellular DNA in a subject being screened for a cancer, and the like,using the 454 sequencing (Roche) (e.g. as described in Margulies, M. etal. Nature 437:376-380 [2005]). 454 sequencing typically involves twosteps. In the first step, DNA is sheared into fragments of approximately300-800 base pairs, and the fragments are blunt-ended. Oligonucleotideadaptors are then ligated to the ends of the fragments. The adaptorsserve as primers for amplification and sequencing of the fragments. Thefragments can be attached to DNA capture beads, e.g.,streptavidin-coated beads using, e.g., Adaptor B, which contains5′-biotin tag. The fragments attached to the beads are PCR amplifiedwithin droplets of an oil-water emulsion. The result is multiple copiesof clonally amplified DNA fragments on each bead. In the second step,the beads are captured in wells (e.g., picoliter-sized wells).Pyrosequencing is performed on each DNA fragment in parallel. Additionof one or more nucleotides generates a light signal that is recorded bya CCD camera in a sequencing instrument. The signal strength isproportional to the number of nucleotides incorporated. Pyrosequencingmakes use of pyrophosphate (PPi) which is released upon nucleotideaddition. PPi is converted to ATP by ATP sulfurylase in the presence ofadenosine 5′ phosphosulfate. Luciferase uses ATP to convert luciferin tooxyluciferin, and this reaction generates light that is measured andanalyzed.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprises obtaining sequence information for thenucleic acids in the test sample e.g. cfDNA in a maternal test sample,cfDNA or cellular DNA in a subject being screened for a cancer, and thelike, using the SOLiD™ technology (Applied Biosystems). In SOLiD™sequencing-by-ligation, genomic DNA is sheared into fragments, andadaptors are attached to the 5′ and 3′ ends of the fragments to generatea fragment library. Alternatively, internal adaptors can be introducedby ligating adaptors to the 5′ and 3′ ends of the fragments,circularizing the fragments, digesting the circularized fragment togenerate an internal adaptor, and attaching adaptors to the 5′ and 3′ends of the resulting fragments to generate a mate-paired library. Next,clonal bead populations are prepared in microreactors containing beads,primers, template, and PCR components. Following PCR, the templates aredenatured and beads are enriched to separate the beads with extendedtemplates. Templates on the selected beads are subjected to a 3′modification that permits bonding to a glass slide. The sequence can bedetermined by sequential hybridization and ligation of partially randomoligonucleotides with a central determined base (or pair of bases) thatis identified by a specific fluorophore. After a color is recorded, theligated oligonucleotide is cleaved and removed and the process is thenrepeated.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample e.g. cfDNA in a maternal test sample, cfDNA orcellular DNA in a subject being screened for a cancer, and the like,using the single molecule, real-time (SMRT™) sequencing technology ofPacific Biosciences. In SMRT sequencing, the continuous incorporation ofdye-labeled nucleotides is imaged during DNA synthesis. Single DNApolymerase molecules are attached to the bottom surface of individualzero-mode wavelength detectors (ZMW detectors) that obtain sequenceinformation while phospholinked nucleotides are being incorporated intothe growing primer strand. A ZMW detector comprises a confinementstructure that enables observation of incorporation of a singlenucleotide by DNA polymerase against a background of fluorescentnucleotides that rapidly diffuse in an out of the ZMW (e.g., inmicroseconds). It typically takes several milliseconds to incorporate anucleotide into a growing strand. During this time, the fluorescentlabel is excited and produces a fluorescent signal, and the fluorescenttag is cleaved off. Measurement of the corresponding fluorescence of thedye indicates which base was incorporated. The process is repeated toprovide a sequence.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample e.g. cfDNA in a maternal test sample, cfDNA orcellular DNA in a subject being screened for a cancer, and the like,using nanopore sequencing (e.g. as described in Soni G V and Meller A.Clin Chem 53: 1996-2001 [2007]). Nanopore sequencing DNA analysistechniques are developed by a number of companies, including, forexample, Oxford Nanopore Technologies (Oxford, United Kingdom),Sequenom, NABsys, and the like. Nanopore sequencing is a single-moleculesequencing technology whereby a single molecule of DNA is sequenceddirectly as it passes through a nanopore. A nanopore is a small hole,typically of the order of 1 nanometer in diameter. Immersion of ananopore in a conducting fluid and application of a potential (voltage)across it results in a slight electrical current due to conduction ofions through the nanopore. The amount of current that flows is sensitiveto the size and shape of the nanopore. As a DNA molecule passes througha nanopore, each nucleotide on the DNA molecule obstructs the nanoporeto a different degree, changing the magnitude of the current through thenanopore in different degrees. Thus, this change in the current as theDNA molecule passes through the nanopore provides a read of the DNAsequence.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprises obtaining sequence information for thenucleic acids in the test sample e.g. cfDNA in a maternal test sample,cfDNA or cellular DNA in a subject being screened for a cancer, and thelike, using the chemical-sensitive field effect transistor (chemFET)array (e.g., as described in U.S. Patent Application Publication No.2009/0026082). In one example of this technique, DNA molecules can beplaced into reaction chambers, and the template molecules can behybridized to a sequencing primer bound to a polymerase. Incorporationof one or more triphosphates into a new nucleic acid strand at the 3′end of the sequencing primer can be discerned as a change in current bya chemFET. An array can have multiple chemFET sensors. In anotherexample, single nucleic acids can be attached to beads, and the nucleicacids can be amplified on the bead, and the individual beads can betransferred to individual reaction chambers on a chemFET array, witheach chamber having a chemFET sensor, and the nucleic acids can besequenced.

In another embodiment, the present method comprises obtaining sequenceinformation for the nucleic acids in the test sample e.g. cfDNA in amaternal test sample, using the Halcyon Molecular's technology, whichuses transmission electron microscopy (TEM). The method, termedIndividual Molecule Placement Rapid Nano Transfer (IMPRNT), comprisesutilizing single atom resolution transmission electron microscopeimaging of high-molecular weight (150 kb or greater) DNA selectivelylabeled with heavy atom markers and arranging these molecules onultra-thin films in ultra-dense (3 nm strand-to-strand) parallel arrayswith consistent base-to-base spacing. The electron microscope is used toimage the molecules on the films to determine the position of the heavyatom markers and to extract base sequence information from the DNA. Themethod is further described in PCT patent publication WO 2009/046445.The method allows for sequencing complete human genomes in less than tenminutes.

In another embodiment, the DNA sequencing technology is the Ion Torrentsingle molecule sequencing, which pairs semiconductor technology with asimple sequencing chemistry to directly translate chemically encodedinformation (A, C, G, T) into digital information (0, 1) on asemiconductor chip. In nature, when a nucleotide is incorporated into astrand of DNA by a polymerase, a hydrogen ion is released as abyproduct. Ion Torrent uses a high-density array of micro-machined wellsto perform this biochemical process in a massively parallel way. Eachwell holds a different DNA molecule. Beneath the wells is anion-sensitive layer and beneath that an ion sensor. When a nucleotide,for example a C, is added to a DNA template and is then incorporatedinto a strand of DNA, a hydrogen ion will be released. The charge fromthat ion will change the pH of the solution, which can be detected byIon Torrent's ion sensor. The sequencer—essentially the world's smallestsolid-state pH meter—calls the base, going directly from chemicalinformation to digital information. The Ion personal Genome Machine(PGM™) sequencer then sequentially floods the chip with one nucleotideafter another. If the next nucleotide that floods the chip is not amatch. No voltage change will be recorded and no base will be called. Ifthere are two identical bases on the DNA strand, the voltage will bedouble, and the chip will record two identical bases called. Directdetection allows recordation of nucleotide incorporation in seconds.

In another embodiment, the present method comprises obtaining sequenceinformation for the nucleic acids in the test sample e.g. cfDNA in amaternal test sample, using sequencing by hybridization.Sequencing-by-hybridization comprises contacting the plurality ofpolynucleotide sequences with a plurality of polynucleotide probes,wherein each of the plurality of polynucleotide probes can be optionallytethered to a substrate. The substrate might be flat surface comprisingan array of known nucleotide sequences. The pattern of hybridization tothe array can be used to determine the polynucleotide sequences presentin the sample. In other embodiments, each probe is tethered to a bead,e.g., a magnetic bead or the like. Hybridization to the beads can bedetermined and used to identify the plurality of polynucleotidesequences within the sample.

In another embodiment, the present method comprises obtaining sequenceinformation for the nucleic acids in the test sample e.g. cfDNA in amaternal test sample, by massively parallel sequencing of millions ofDNA fragments using Illumina's sequencing-by-synthesis and reversibleterminator-based sequencing chemistry (e.g. as described in Bentley etal., Nature 6:53-59 [2009]). Template DNA can be genomic DNA e.g. cfDNA.In some embodiments, genomic DNA from isolated cells is used as thetemplate, and it is fragmented into lengths of several hundred basepairs. In other embodiments, cfDNA is used as the template, andfragmentation is not required as cfDNA exists as short fragments. Forexample fetal cfDNA circulates in the bloodstream as fragmentsapproximately 170 base pairs (bp) in length (Fan et al., Clin Chem56:1279-1286 [2010]), and no fragmentation of the DNA is required priorto sequencing. Illumina's sequencing technology relies on the attachmentof fragmented genomic DNA to a planar, optically transparent surface onwhich oligonucleotide anchors are bound. Template DNA is end-repaired togenerate 5′-phosphorylated blunt ends, and the polymerase activity ofKlenow fragment is used to add a single A base to the 3′ end of theblunt phosphorylated DNA fragments. This addition prepares the DNAfragments for ligation to oligonucleotide adapters, which have anoverhang of a single T base at their 3′ end to increase ligationefficiency. The adapter oligonucleotides are complementary to theflow-cell anchors. Under limiting-dilution conditions, adapter-modified,single-stranded template DNA is added to the flow cell and immobilizedby hybridization to the anchors. Attached DNA fragments are extended andbridge amplified to create an ultra-high density sequencing flow cellwith hundreds of millions of clusters, each containing ˜1,000 copies ofthe same template. In one embodiment, the randomly fragmented genomicDNA e.g. cfDNA, is amplified using PCR before it is subjected to clusteramplification. Alternatively, an amplification-free genomic librarypreparation is used, and the randomly fragmented genomic DNA e.g. cfDNAis enriched using the cluster amplification alone (Kozarewa et al.,Nature Methods 6:291-295 [2009]). The templates are sequenced using arobust four-color DNA sequencing-by-synthesis technology that employsreversible terminators with removable fluorescent dyes. High-sensitivityfluorescence detection is achieved using laser excitation and totalinternal reflection optics. Short sequence reads of about 20-40 bp e.g.36 bp, are aligned against a repeat-masked reference genome and uniquemapping of the short sequence reads to the reference genome areidentified using specially developed data analysis pipeline software.Non-repeat-masked reference genomes can also be used. Whetherrepeat-masked or non-repeat-masked reference genomes are used, onlyreads that map uniquely to the reference genome are counted. Aftercompletion of the first read, the templates can be regenerated in situto enable a second read from the opposite end of the fragments. Thus,either single-end or paired end sequencing of the DNA fragments can beused. Partial sequencing of DNA fragments present in the sample isperformed, and sequence tags comprising reads of predetermined lengthe.g. 36 bp, are mapped to a known reference genome are counted. In oneembodiment, the reference genome sequence is the NCBI36/hg18 sequence,which is available on the world wide web atgenome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).Alternatively, the reference genome sequence is the GRCh37/hg19, whichis available on the world wide web at genome.ucsc.edu/cgi-bin/hgGateway.Other sources of public sequence information include GenBank, dbEST,dbSTS, EMBL (the European Molecular Biology Laboratory), and the DDBJ(the DNA Databank of Japan). A number of computer algorithms areavailable for aligning sequences, including without limitation BLAST(Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993),FASTA (Person & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology10:R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego, Calif.,USA). In one embodiment, one end of the clonally expanded copies of theplasma cfDNA molecules is sequenced and processed by bioinformaticalignment analysis for the Illumina Genome Analyzer, which uses theEfficient Large-Scale Alignment of Nucleotide Databases (ELAND)software.

In some embodiments of the methods described herein, the mapped sequencetags comprise sequence reads of about 20 bp, about 25 bp, about 30 bp,about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp,about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp,about 130, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp. It isexpected that technological advances will enable single-end reads ofgreater than 500 bp enabling for reads of greater than about 1000 bpwhen paired end reads are generated. In one embodiment, the mappedsequence tags comprise sequence reads that are 36 bp. Mapping of thesequence tags is achieved by comparing the sequence of the tag with thesequence of the reference to determine the chromosomal origin of thesequenced nucleic acid (e.g. cfDNA) molecule, and specific geneticsequence information is not needed. A small degree of mismatch (0-2mismatches per sequence tag) may be allowed to account for minorpolymorphisms that may exist between the reference genome and thegenomes in the mixed sample.

A plurality of sequence tags are typically obtained per sample. In someembodiments, at least about 3×10⁶ sequence tags, at least about 5×10⁶sequence tags, at least about 8×10⁶ sequence tags, at least about 10×10⁶sequence tags, at least about 15×10⁶ sequence tags, at least about20×10⁶ sequence tags, at least about 30×10⁶ sequence tags, at leastabout 40×10⁶ sequence tags, or at least about 50×10⁶ sequence tagscomprising between 20 and 40 bp reads e.g. 36 bp, are obtained frommapping the reads to the reference genome per sample. In one embodiment,all the sequence reads are mapped to all regions of the referencegenome. In one embodiment, the tags that have been mapped to all regionse.g. all chromosomes, of the reference genome are counted, and the CNVi.e. the over- or under-representation of a sequence of interest e.g. achromosome or portion thereof, in the mixed DNA sample is determined.The method does not require differentiation between the two genomes.

The accuracy required for correctly determining whether a CNV e.g.aneuploidy, is present or absent in a sample, is predicated on thevariation of the number of sequence tags that map to the referencegenome among samples within a sequencing run (inter-chromosomalvariability), and the variation of the number of sequence tags that mapto the reference genome in different sequencing runs (inter-sequencingvariability). For example, the variations can be particularly pronouncedfor tags that map to GC-rich or GC-poor reference sequences. Othervariations can result from using different protocols for the extractionand purification of the nucleic acids, the preparation of the sequencinglibraries, and the use of different sequencing platforms. The presentmethod uses sequence doses (chromosome doses, or segment doses) based onthe knowledge of normalizing sequences (normalizing chromosome sequencesor normalizing segment sequences), to intrinsically account for theaccrued variability stemming from interchromosomal (intra-run), andinter-sequencing (inter-run) and platform-dependent variability.Chromosome doses are based on the knowledge of a normalizing chromosomesequence, which can be composed of a single chromosome, or of two ormore chromosomes selected from chromosomes 1-22, X, and Y.Alternatively, normalizing chromosome sequences can be composed of asingle chromosome segment, or of two or more segments of one chromosomeor of two or more chromosomes. Segment doses are based on the knowledgeof a normalizing segment sequence, which can be composed of a singlesegment of any one chromosome, or of two or more segments of any two ormore of chromosomes 1-22, X, and Y.

Singleplex Sequencing

FIG. 4 illustrates a flow chart of an embodiment of the method wherebymarker nucleic acids are combined with source sample nucleic acids of asingle sample to assay for a genetic abnormality while determining theintegrity of the biological source sample. In step 410, a biologicalsource sample comprising genomic nucleic acids is obtained. In step 420,marker nucleic acids are combined with the biological source sample toprovide a marked sample. A sequencing library of a mixture of clonallyamplified source sample genomic and marker nucleic acids is prepared instep 430, and the library is sequenced in a massively parallel fashionin step 440 to provide sequencing information pertaining to the sourcegenomic and marker nucleic acids of the sample. Massively parallelsequencing methods provide sequencing information as sequence reads,which are mapped to one or more reference genomes to generate sequencetags that can be analyzed. In step 450, all sequencing information isanalyzed, and based on the sequencing information pertaining to themarker molecules, the integrity of the source sample is verified in step460. Verification of source sample integrity is accomplished bydetermining a correspondence between the sequencing information obtainedfor the maker molecule at step 450 and the known sequence of the markermolecule that was added to the original source sample at step 420. Thesame process can be applied to multiple samples that are sequencedseparately, with each sample comprising molecules having sequencesunique to the sample i.e. one sample is marked with a unique markermolecule and it is sequenced separately from other samples in a flowcell or slide of a sequencer. If the integrity of the sample isverified, the sequencing information pertaining to the genomic nucleicacids of the sample can be analyzed to provide information e.g. aboutthe status of the subject from which the source sample was obtained. Forexample, if the integrity of the sample is verified, the sequencinginformation pertaining to the genomic nucleic acids is analyzed todetermine the presence or absence of a chromosomal abnormality. If theintegrity of the sample is not verified, the sequencing information isdisregarded.

The method depicted in FIG. 4 is also applicable to bioassays thatcomprise singleplex sequencing of single molecules e.g. tSMS by Helicos,SMRT by Pacific Biosciences, BASE by Oxford Nanopore, and othertechnologies such as that suggested by IBM, which do not requirepreparation of libraries.

Multiplex Sequencing

The large number of sequence reads that can be obtained per sequencingrun permits the analysis of pooled samples i.e. multiplexing, whichmaximizes sequencing capacity and reduces workflow. For example, themassively parallel sequencing of eight libraries performed using theeight lane flow cell of the Illumina Genome Analyzer can be multiplexedto sequence two or more samples in each lane such that 16, 24, 32 etc.or more samples can be sequenced in a single run. Parallelizingsequencing for multiple samples i.e. multiplex sequencing, requires theincorporation of sample-specific index sequences, also known asbarcodes, during the preparation of sequencing libraries. Sequencingindexes are distinct base sequences of about 5, about 10, about 15,about 20 about 25, or more bases that are added at the 3′ end of thegenomic and marker nucleic acid. The multiplexing system enablessequencing of hundreds of biological samples within a single sequencingrun. The preparation of indexed sequencing libraries for sequencing ofclonally amplified sequences can be performed by incorporating the indexsequence into one of the PCR primers used for cluster amplification.Alternatively, the index sequence can be incorporated into the adaptor,which is ligated to the cfDNA prior to the PCR amplification. Indexedlibraries for single molecule sequencing can be created by incorporatingthe index sequence at the 3′ end of the marker and genomic molecule or5′ to the addition of a sequence needed for hybridization to the flowcell anchors e.g. addition of the polyA tail for single moleculesequencing using the tSMS. Sequencing of the uniquely marked indexednucleic acids provides index sequence information that identifiessamples in the pooled sample libraries, and sequence information ofmarker molecules correlates sequencing information of the genomicnucleic acids to the sample source. In embodiments wherein the multiplesamples are sequenced individually i.e. singleplex sequencing, markerand genomic nucleic acid molecules of each sample need only be modifiedto contain the adaptor sequences as required by the sequencing platformand exclude the indexing sequences.

FIG. 5 provides a flowchart of an embodiment 500 of the method forverifying the integrity of samples that are subjected to a multistepmultiplex sequencing bioassay i.e. nucleic acids from individual samplesare combined and sequenced as a complex mixture. In step 510, aplurality of biological source samples each comprising genomic nucleicacids is obtained. In step 520, unique marker nucleic acids are combinedwith each of the biological source samples to provide a plurality ofuniquely marked samples. A sequencing library of sample genomic andmarker nucleic acids is prepared in step 530 for each of the uniquelymarked samples. Library preparation of samples that are destined toundergo multiplexed sequencing comprises the incorporation of distinctindexing tags into the sample and marker nucleic acids of each of theuniquely marked samples to provide samples whose source nucleic acidsequences can be correlated with the corresponding marker nucleic acidsequences and identified in complex solutions. In embodiments of themethod comprising marker molecules that can be enzymatically modified,e.g. DNA, indexing molecules can be incorporated at the 3′ of the sampleand marker molecules by ligating sequenceable adaptor sequencescomprising the indexing sequences. In embodiments of the methodcomprising marker molecules that cannot be enzymatically modified, e.g.DNA analogs that do not have a phosphate backbone, indexing sequencesare incorporated at the 3′ of the analog marker molecules duringsynthesis. Sequencing libraries of two or more samples are pooled andloaded on the flow cell of the sequencer where they are sequenced in amassively parallel fashion in step 540. In step 550, all sequencinginformation is analyzed, and based on the sequencing informationpertaining to the marker molecules; the integrity of the source sampleis verified in step 560. Verification of the integrity of each of theplurality of source samples is accomplished by first grouping sequencetags associated with identical index sequences to associate the genomicand marker sequences and distinguish sequences belonging to each of thelibraries made from genomic molecules of a plurality of samples.Analysis of the grouped marker and genomic sequences is then performedto verify that the sequence obtained for the marker moleculescorresponds to the known unique sequence added to the correspondingsource sample. If the integrity of the sample is verified, thesequencing information pertaining to the genomic nucleic acids of thesample can be analyzed to provide genetic information about the subjectfrom which the source sample was obtained. For example, if the integrityof the sample is verified, the sequencing information pertaining to thegenomic nucleic acids is analyzed to determine the presence or absenceof a chromosomal abnormality. The absence of a correspondence betweenthe sequencing information and known sequence of the marker molecule isindicative of a sample mix-up, and the accompanying sequencinginformation pertaining to the genomic cfDNA molecules is disregarded.

Determination of CNV for Prenatal Diagnoses

Cell-free fetal DNA and RNA circulating in maternal blood can be usedfor the early non-invasive prenatal diagnosis (NIPD) of an increasingnumber of genetic conditions, both for pregnancy management and to aidreproductive decision-making. The presence of cell-free DNA circulatingin the bloodstream has been known for over 50 years. More recently,presence of small amounts of circulating fetal DNA was discovered in thematernal bloodstream during pregnancy (Lo et al., Lancet 350:485-487[1997]). Thought to originate from dying placental cells, cell-freefetal DNA (cfDNA) has been shown to consists of short fragmentstypically fewer than 200 bp in length Chan et al., Clin Chem 50:88-92[2004]), which can be discerned as early as 4 weeks gestation (Illaneset al., Early Human Dev 83:563-566 [2007]), and known to be cleared fromthe maternal circulation within hours of delivery (Lo et al., Am J HumGenet 64:218-224 [1999]). In addition to cfDNA, fragments of cell-freefetal RNA (cfRNA) can also be discerned in the maternal bloodstream,originating from genes that are transcribed in the fetus or placenta.The extraction and subsequent analysis of these fetal genetic elementsfrom a maternal blood sample offers novel opportunities for NIPD.

The present method is a polymorphism-independent method that for use inNIPD and that does not require that the fetal cfDNA be distinguishedfrom the maternal cfDNA to enable the determination of a fetalaneuploidy. In some embodiments, the aneuploidy is a completechromosomal trisomy or monosomy, or a partial trisomy or monosomy.Partial aneuploidies are caused by loss or gain of part of a chromosome,and encompass chromosomal imbalances resulting from unbalancedtranslocations, unbalanced inversions, deletions and insertions. By far,the most common known aneuploidy compatible with life is trisomy 21 i.e.Down Syndrome (DS), which is caused by the presence of part or all ofchromosome 21. Rarely, DS can be caused by an inherited or sporadicdefect whereby an extra copy of all or part of chromosome 21 becomesattached to another chromosome (usually chromosome 14) to form a singleaberrant chromosome. DS is associated with intellectual impairment,severe learning difficulties and excess mortality caused by long-termhealth problems such as heart disease. Other aneuploidies with knownclinical significance include Edward syndrome (trisomy 18) and PatauSyndrome (trisomy 13), which are frequently fatal within the first fewmonths of life. Abnormalities associated with the number of sexchromosomes are also known and include monosomy X e.g. Turner syndrome(XO), and triple X syndrome (XXX) in female births and Kleinefeltersyndrome (XXY) and XYY syndrome in male births, which are all associatedwith various phenotypes including sterility and reduction inintellectual skills. Monosomy X [45,X] is a common cause of earlypregnancy loss accounting for about 7% of spontaneous abortions. Basedon the liveborn frequency of 45,X (also called Turner syndrome) of1-2/10,000, it is estimated that less than 1% of 45,X conceptuses willsurvive to term. About 30% of Turners syndrome patients are mosaic withboth a 45,X cell line and either a 46,XX cell line or one containing arearranged X chromosome (Hook and Warburton 1983). The phenotype in aliveborn infant is relatively mild considering the high embryoniclethality and it has been hypothesized that possibly all livebornfemales with Turner syndrome carry a cell line containing two sexchromosomes. Monosomy X can occur in females as 45,X or as 45,X/46XX,and in males as 45,X/46XY. Autosomal monosomies in human are generallysuggested to be incompatible with life; however, there is quite a numberof cytogenetic reports describing full monosomy of one chromosome 21 inlive born children (Vosranova I et al., Molecular Cytogen. 1:13 [2008];Joosten et al., Prenatal Diagn. 17:271-5 [1997]. The method describedherein can be used to diagnose these and other chromosomal abnormalitiesprenatally.

According to some embodiments the methods disclosed herein can determinethe presence or absence of chromosomal trisomies of any one ofchromosomes 1-22, X and Y. Examples of chromosomal trisomies that can bedetected according to the present method include without limitationtrisomy 21 (T21; Down Syndrome), trisomy 18 (T18; Edward's Syndrome),trisomy 16 (T16), trisomy 20 (T20), trisomy 22 (T22; Cat Eye Syndrome),trisomy 15 (T15; Prader Willi Syndrome), trisomy 13 (T13; PatauSyndrome), trisomy 8 (T8; Warkany Syndrome), trisomy 9, and the XXY(Kleinefelter Syndrome), XYY, or XXX trisomies. Complete trisomies ofother autosomes existing in a non-mosaic state are lethal, but can becompatible with life when present in a mosaic state. It will beappreciated that various complete trisomies, whether existing in amosaic or non-mosaic state, and partial trisomies can be determined infetal cfDNA according to the teachings provided herein.

Non-limiting examples of partial trisomies that can be determined by thepresent method include, but are not limited to, partial trisomy 1q32-44,trisomy 9 p, trisomy 4 mosaicism, trisomy 17p, partial trisomy4q26-qter, partial 2p trisomy, partial trisomy 1q, and/or partialtrisomy 6p/monosomy 6q.

The methods disclosed herein can be also used to determine chromosomalmonosomy X, chromosomal monosomy 21, and partial monosomies such as,monosomy 13, monosomy 15, monosomy 16, monosomy 21, and monosomy 22,which are known to be involved in pregnancy miscarriage. Partialmonosomy of chromosomes typically involved in complete aneuploidy canalso be determined by the method described herein. Non-limiting examplesof deletion syndromes that can be determined according to the presentmethod include syndromes caused by partial deletions of chromosomes.Examples of partial deletions that can be determined according to themethods described herein include without limitation partial deletions ofchromosomes 1, 4, 5, 7, 11, 18, 15, 13, 17, 22 and 10, which aredescribed in the following.

1q21.1 deletion syndrome or 1q21.1 (recurrent) microdeletion is a rareaberration of chromosome 1. Next to the deletion syndrome, there is alsoa 1q21.1 duplication syndrome. While there is a part of the DNA missingwith the deletion syndrome on a particular spot, there are two or threecopies of a similar part of the DNA on the same spot with theduplication syndrome. Literature refers to both the deletion and theduplication as the 1q21.1 copy-number variations (CNV). The 1q21.1deletion can be associated with the TAR Syndrome (Thrombocytopenia withAbsent radius).

Wolf-Hirschhorn syndrome (WHS) (OMIN #194190) is a contiguous genedeletion syndrome associated with a hemizygous deletion of chromosome4p16.3. Wolf-Hirschhorn syndrome is a congenital malformation syndromecharacterized by pre- and postnatal growth deficiency, developmentaldisability of variable degree, characteristic craniofacial features(‘Greek warrior helmet’ appearance of the nose, high forehead, prominentglabella, hypertelorism, high-arched eyebrows, protruding eyes,epicanthal folds, short philtrum, distinct mouth with downturnedcorners, and micrognathia), and a seizure disorder.

Partial deletion of chromosome 5, also known as 5p− or 5p minus, andnamed Cris du Chat syndrome (OMIN #123450), is caused by a deletion ofthe short arm (p arm) of chromosome 5 (5p15.3-p15.2). Infants with thiscondition often have a high-pitched cry that sounds like that of a cat.The disorder is characterized by intellectual disability and delayeddevelopment, small head size (microcephaly), low birth weight, and weakmuscle tone (hypotonia) in infancy, distinctive facial features andpossibly heart defects.

Williams-Beuren Syndrome also known as chromosome 7q11.23 deletionsyndrome (OMIN 194050) is a contiguous gene deletion syndrome resultingin a multisystem disorder caused by hemizygous deletion of 1.5 to 1.8 Mbon chromosome 7q11.23, which contains approximately 28 genes.

Jacobsen Syndrome, also known as 11q deletion disorder, is a rarecongenital disorder resulting from deletion of a terminal region ofchromosome 11 that includes band 11q24.1. It can cause intellectualdisabilities, a distinctive facial appearance, and a variety of physicalproblems including heart defects and a bleeding disorder.

Partial monosomy of chromosome 18, known as monosomy 18p is a rarechromosomal disorder in which all or part of the short arm (p) ofchromosome 18 is deleted (monosomic). The disorder is typicallycharacterized by short stature, variable degrees of mental retardation,speech delays, malformations of the skull and facial (craniofacial)region, and/or additional physical abnormalities. Associatedcraniofacial defects may vary greatly in range and severity from case tocase.

Conditions caused by changes in the structure or number of copies ofchromosome 15 include Angelman Syndrome and Prader-Willi Syndrome, whichinvolve a loss of gene activity in the same part of chromosome 15, the15q11-q13 region. It will be appreciated that several translocations andmicrodeletions can be asymptomatic in the carrier parent, yet can causea major genetic disease in the offspring. For example, a healthy motherwho carries the 15q11-q13 microdeletion can give birth to a child withAngelman syndrome, a severe neurodegenerative disorder. Thus, themethods, apparatus and systems described herein can be used to identifysuch a partial deletion and other deletions in the fetus.

Partial monosomy 13q is a rare chromosomal disorder that results when apiece of the long arm (q) of chromosome 13 is missing (monosomic).Infants born with partial monosomy 13q may exhibit low birth weight,malformations of the head and face (craniofacial region), skeletalabnormalities (especially of the hands and feet), and other physicalabnormalities. Mental retardation is characteristic of this condition.The mortality rate during infancy is high among individuals born withthis disorder. Almost all cases of partial monosomy 13q occur randomlyfor no apparent reason (sporadic).

Smith-Magenis syndrome (SMS—OMIM #182290) is caused by a deletion, orloss of genetic material, on one copy of chromosome 17. This well-knownsyndrome is associated with developmental delay, mental retardation,congenital anomalies such as heart and kidney defects, andneurobehavioral abnormalities such as severe sleep disturbances andself-injurious behavior. Smith-Magenis syndrome (SMS) is caused in mostcases (90%) by a 3.7-Mb interstitial deletion in chromosome 17p11.2.

22q11.2 deletion syndrome, also known as DiGeorge syndrome, is asyndrome caused by the deletion of a small piece of chromosome 22. Thedeletion (22q11.2) occurs near the middle of the chromosome on the longarm of one of the pair of chromosome. The features of this syndrome varywidely, even among members of the same family, and affect many parts ofthe body. Characteristic signs and symptoms may include birth defectssuch as congenital heart disease, defects in the palate, most commonlyrelated to neuromuscular problems with closure (velo-pharyngealinsufficiency), learning disabilities, mild differences in facialfeatures, and recurrent infections. Microdeletions in chromosomal region22q11.2 are associated with a 20 to 30-fold increased risk ofschizophrenia.

Deletions on the short arm of chromosome 10 are associated with aDiGeorge Syndrome like phenotype. Partial monosomy of chromosome 10p israre but has been observed in a portion of patients showing features ofthe DiGeorge Syndrome.

In one embodiment, the methods, apparatus, and systems described hereinis used to determine partial monosomies including but not limited topartial monosomy of chromosomes 1, 4, 5, 7, 11, 18, 15, 13, 17, 22 and10, e.g. partial monosomy 1q21.11, partial monosomy 4p16.3, partialmonosomy 5p15.3-p15.2, partial monosomy 7q11.23, partial monosomy11q24.1, partial monosomy 18p, partial monosomy of chromosome 15(15q11-q13), partial monosomy 13q, partial monosomy 17p11.2, partialmonosomy of chromosome 22 (22q11.2), and partial monosomy 10p can alsobe determined using the method.

Other partial monosomies that can be determined according to the methodsdescribed herein include unbalanced translocation t(8;11)(p23.2;p15.5);11q23 microdeletion; 17p11.2 deletion; 22q13.3 deletion; Xp22.3microdeletion; 10p14 deletion; 20p microdeletion,[del(22)(q11.2q11.23)], 7q11.23 and 7q36 deletions; 1p36 deletion; 2pmicrodeletion; neurofibromatosis type 1 (17q11.2 microdeletion), Yqdeletion; 4p16.3 microdeletion; 1p36.2 microdeletion; 11q14 deletion;19q13.2 microdeletion; Rubinstein-Taybi (16p13.3 microdeletion); 7p21microdeletion; Miller-Dieker syndrome (17p13.3); and 2q37 microdeletion.Partial deletions can be small deletions of part of a chromosome, orthey can be microdeletions of a chromosome where the deletion of asingle gene can occur.

Several duplication syndromes caused by the duplication of part ofchromosome arms have been identified (see OMIN [Online MendelianInheritance in Man viewed online at ncbi.nlm.nih.gov/omim]). In oneembodiment, the present method can be used to determine the presence orabsence of duplications and/or multiplications of segments of any one ofchromosomes 1-22, X and Y. Non-limiting examples of duplicationssyndromes that can be determined according to the present method includeduplications of part of chromosomes 8, 15, 12, and 17, which aredescribed in the following.

8p23.1 duplication syndrome is a rare genetic disorder caused by aduplication of a region from human chromosome 8. This duplicationsyndrome has an estimated prevalence of 1 in 64,000 births and is thereciprocal of the 8p23.1 deletion syndrome. The 8p23.1 duplication isassociated with a variable phenotype including one or more of speechdelay, developmental delay, mild dysmorphism, with prominent foreheadand arched eyebrows, and congenital heart disease (CHD).

Chromosome 15q Duplication Syndrome (Dup15q) is a clinicallyidentifiable syndrome which results from duplications of chromosome15q11-13.1 Babies with Dup15q usually have hypotonia (poor muscle tone),growth retardation; they may be born with a cleft lip and/or palate ormalformations of the heart, kidneys or other organs; they show somedegree of cognitive delay/disability (mental retardation), speech andlanguage delays, and sensory processing disorders.

Pallister Killian syndrome is a result of extra #12 chromosome material.There is usually a mixture of cells (mosaicism), some with extra #12material, and some that are normal (46 chromosomes without the extra #12material). Babies with this syndrome have many problems including severemental retardation, poor muscle tone, “coarse” facial features, and aprominent forehead. They tend to have a very thin upper lip with athicker lower lip and a short nose. Other health problems includeseizures, poor feeding, stiff joints, cataracts in adulthood, hearingloss, and heart defects. Persons with Pallister Killian have a shortenedlifespan.

Individuals with the genetic condition designated as dup(17)(p11.2p11.2)or dup 17p carry extra genetic information (known as a duplication) onthe short arm of chromosome 17. Duplication of chromosome 17p11.2underlies Potocki-Lupski syndrome (PTLS), which is a newly recognizedgenetic condition with only a few dozen cases reported in the medicalliterature. Patients who have this duplication often have low muscletone, poor feeding, and failure to thrive during infancy, and alsopresent with delayed development of motor and verbal milestones. Manyindividuals who have PTLS have difficulty with articulation and languageprocessing. In addition, patients may have behavioral characteristicssimilar to those seen in persons with autism or autism-spectrumdisorders. Individuals with PTLS may have heart defects and sleep apnea.A duplication of a large region in chromosome 17p12 that includes thegene PMP22 is known to cause Charcot-Marie Tooth disease.

CNV have been associated with stillbirths. However, due to inherentlimitations of conventional cytogenetics, the contribution of CNV tostillbirth is thought to be underrepresented (Harris et al., PrenatalDiagn 31:932-944 [2011]). As is shown in the examples and describedelsewhere herein, the present method is capable of determining thepresence of partial aneuploidies e.g. deletions and multiplications ofchromosome segments, and can be used to identify and determine thepresence or absence of CNV that are associated with stillbirths.

Determination of Complete Fetal Chromosomal Aneuploidies

In one embodiment, methods are provided for determining the presence orabsence of any one or more different complete fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acid molecules. Preferably, the method determines the presenceor absence of any four or more different complete chromosomalaneuploidies. The steps of the method comprise (a) obtaining sequenceinformation for the fetal and maternal nucleic acids in the maternaltest sample; and (b) using the sequence information to identify a numberof sequence tags for each of any one or more chromosomes of interestselected from chromosomes 1-22, X and Y and to identify a number ofsequence tags for a normalizing chromosome sequence for each of the anyone or more chromosomes of interest. The normalizing chromosome sequencecan be a single chromosome, or it can be a group of chromosomes selectedfrom chromosomes 1-22, X, and Y. The method further uses in step (c) thenumber of sequence tags identified for each of the any one or morechromosomes of interest and the number of sequence tags identified foreach normalizing chromosome sequence to calculate a single chromosomedose for each of the any one or more chromosomes of interest; and (d)compares each of the single chromosome doses for each of the any one ormore chromosomes of interest to a threshold value for each of the one ormore chromosomes of interest, thereby determining the presence orabsence of any one or more complete different fetal chromosomalaneuploidies in the maternal test sample.

In some embodiments, step (c) comprises calculating a single chromosomedose for each chromosomes of interest as the ratio of the number ofsequence tags identified for each of the chromosomes of interest and thenumber of sequence tags identified for the normalizing chromosome foreach of the chromosomes of interest.

In other embodiments, step (c) comprises calculating a single chromosomedose for each of the chromosomes of interest as the ratio of the numberof sequence tags identified for each of the chromosomes of interest andthe number of sequence tags identified for the normalizing chromosomefor each of the chromosomes of interest. In other embodiments, step (c)comprises calculating a sequence tag ratio for a chromosome of interestby relating the number of sequence tags obtained for the chromosome ofinterest to the length of the chromosome of interest, and relating thenumber of tags for the corresponding normalizing chromosome sequence forthe chromosome of interest to the length of the normalizing chromosomesequence, and calculating a chromosome dose for the chromosome ofinterest as a ratio of the sequence tags density of the chromosome ofinterest and the sequence tag density for the normalizing sequence. Thecalculation is repeated for each of all chromosomes of interest. Steps(a)-(d) can be repeated for test samples from different maternalsubjects.

An example of the embodiment whereby four or more complete fetalchromosomal aneuploidies are determined in a maternal test samplecomprising a mixture of fetal and maternal cell-free DNA molecules,comprises: (a) sequencing at least a portion of cell-free DNA moleculesto obtain sequence information for the fetal and maternal cell-free DNAmolecules in the test sample; (b) using the sequence information toidentify a number of sequence tags for each of any twenty or morechromosomes of interest selected from chromosomes 1-22, X, and Y and toidentify a number of sequence tags for a normalizing chromosome for eachof the twenty or more chromosomes of interest; (c) using the number ofsequence tags identified for each of the twenty or more chromosomes ofinterest and the number of sequence tags identified for each thenormalizing chromosome to calculate a single chromosome dose for each ofthe twenty or more chromosomes of interest; and (d) comparing each ofthe single chromosome doses for each of the twenty or more chromosomesof interest to a threshold value for each of the twenty or morechromosomes of interest, and thereby determining the presence or absenceof any twenty or more different complete fetal chromosomal aneuploidiesin the test sample.

In another embodiment, the method for determining the presence orabsence of any one or more different complete fetal chromosomalaneuploidies in a maternal test sample as described above uses anormalizing segment sequence for determining the dose of the chromosomeof interest. In this instance, the method comprises (a) obtainingsequence information for said fetal and maternal nucleic acids in saidsample; (b) using said sequence information to identify a number ofsequence tags for each of any one or more chromosomes of interestselected from chromosomes 1-22, X and Y and to identify a number ofsequence tags for a normalizing segment sequence for each of said anyone or more chromosomes of interest. The normalizing segment sequencecan be a single segment of a chromosome or it can be a group of segmentsform one or more different chromosomes. The method further uses in step(c) the number of sequence tags identified for each of said any one ormore chromosomes of interest and said number of sequence tags identifiedfor said normalizing segment sequence to calculate a single chromosomedose for each of said any one or more chromosomes of interest; and (d)comparing each of said single chromosome doses for each of said any oneor more chromosomes of interest to a threshold value for each of saidone or more chromosomes of interest, and thereby determining thepresence or absence of one or more different complete fetal chromosomalaneuploidies in said sample.

In some embodiments, step (c) comprises calculating a single chromosomedose for each of said chromosomes of interest as the ratio of the numberof sequence tags identified for each of said chromosomes of interest andthe number of sequence tags identified for said normalizing segmentsequence for each of said chromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tagratio for a chromosome of interest by relating the number of sequencetags obtained for the chromosome of interest to the length of thechromosome of interest, and relating the number of tags for thecorresponding normalizing segment sequence for the chromosome ofinterest to the length of the normalizing segment sequence, andcalculating a chromosome dose for the chromosome of interest as a ratioof the sequence tags density of the chromosome of interest and thesequence tag density for the normalizing segment sequence. Thecalculation is repeated for each of all chromosomes of interest. Steps(a)-(d) can be repeated for test samples from different maternalsubjects.

A means for comparing chromosome doses of different sample sets isprovided by determining a normalized chromosome value (NCV), whichrelates the chromosome dose in a test sample to the mean of the of thecorresponding chromosome dose in a set of qualified samples. The NCV iscalculated as:

${NCV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thchromosome dose in a set of qualified samples, and x_(ij) is theobserved j-th chromosome dose for test sample i.

In some embodiments, the presence or absence of at least one completefetal chromosomal aneuploidy is determined. In other embodiments, thepresence or absence of at least two, at least three, at least four, atleast five, at least six, at least seven, at least eight, at least nine,at least ten, at least eleven, at least twelve, at least thirteen, atleast fourteen, at least fifteen, at least sixteen, at least seventeen,at least eighteen, at least nineteen, at least twenty, at leasttwenty-one, at least twenty-two, at least twenty-three, or twenty-fourcomplete fetal chromosomal aneuploidies are determined in a sample,wherein twenty-two of the complete fetal chromosomal aneuploidiescorrespond to complete chromosomal aneuploidies of any one or more ofthe autosomes; the twenty-third and twenty fourth chromosomal aneuploidycorrespond to a complete fetal chromosomal aneuploidy of chromosomes Xand Y. As aneuploidies of sex chromosomes can comprise tetrasomies,pentasomies and other polysomies, the number of different completechromosomal aneuploidies that can be determined according to the presentmethod may be at least 24, at least 25, at least 26, at least 27, atleast 28, at least 29, or at least 30 complete chromosomal aneuploidies.Thus, the number of different complete fetal chromosomal aneuploidiesthat are determined is related to the number of chromosomes of interestthat are selected for analysis.

In one embodiment, determining the presence or absence of any one ormore different complete fetal chromosomal aneuploidies in a maternaltest sample as described above uses a normalizing segment sequence forone chromosome of interest, which is selected from chromosomes 1-22, X,and Y. In other embodiments, two or more chromosomes of interest areselected from any two or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In oneembodiment, any one or more chromosomes of interest are selected fromchromosomes 1-22, X, and Y comprise at least twenty chromosomes selectedfrom chromosomes 1-22, X, and Y, and wherein the presence or absence ofat least twenty different complete fetal chromosomal aneuploidies isdetermined. In other embodiments, any one or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y is all of chromosomes1-22, X, and Y, and wherein the presence or absence of complete fetalchromosomal aneuploidies of all of chromosomes 1-22, X, and Y isdetermined. Complete different fetal chromosomal aneuploidies that canbe determined include complete chromosomal trisomies, completechromosomal monosomies and complete chromosomal polysomies. Examples ofcomplete fetal chromosomal aneuploidies include without limitationtrisomies of any one or more of the autosomes e.g. trisomy 2, trisomy 8,trisomy 9, trisomy 20, trisomy 21, trisomy 13, trisomy 16, trisomy 18,trisomy 22; trisomies of the sex chromosomes e.g. 47,XXY, 47 XXX, and 47XYY; tetrasomies of sex chromosomes e.g. 48,XXYY, 48,XXXY, 48XXXX, and48,XYYY; pentasomies of sex chromosomes e.g. 49,XXXYY 49,XXXXY,49,XXXXX, 49,XYYYY; and monosomy X. Other complete fetal chromosomalaneuploidies that can be determined according to the present method aredescribed below.

Determination of Partial Fetal Chromosomal Aneuploidies

In another embodiment, method are provided for determining the presenceor absence of any one or more different partial fetal chromosomalaneuploidies in a maternal test sample comprising fetal and maternalnucleic acid molecules. The steps of the method comprise (a) obtainingsequence information for the fetal and maternal nucleic acids in saidsample; and (b) using the sequence information to identify a number ofsequence tags for each of any one or more segments of any one or morechromosomes of interest selected from chromosomes 1-22, X, and Y and toidentify a number of sequence tags for a normalizing segment sequencefor each of said any one or more segments of any one or more chromosomesof interest. The normalizing segment sequence can be a single segment ofa chromosome or it can be a group of segments form one or more differentchromosomes. The method further uses in step (c) the number of sequencetags identified for each of any one or more segments of any one or morechromosomes of interest and the number of sequence tags identified forthe normalizing segment sequence to calculate a single segment dose foreach of any one or more segments of any one or more chromosome ofinterest; and (d) comparing each of the single chromosome doses for eachof any one or more segments of any one or more chromosomes of interestto a threshold value for each of said any one or more chromosomalsegments of any one or more chromosome of interest, and therebydetermining the presence or absence of one or more different partialfetal chromosomal aneuploidies in said sample.

In some embodiments, step (c) comprises calculating a single segmentdose for each of any one or more segments of any one or more chromosomesof interest as the ratio of the number of sequence tags identified foreach of any one or more segments of any one or more chromosomes ofinterest and the number of sequence tags identified for the normalizingsegment sequence for each of any one or more segments of any one or morechromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tagratio for a segment of interest by relating the number of sequence tagsobtained for the segment of interest to the length of the segment ofinterest, and relating the number of tags for the correspondingnormalizing segment sequence for the segment of interest to the lengthof the normalizing segment sequence, and calculating a segment dose forthe segment of interest as a ratio of the sequence tags density of thesegment of interest and the sequence tag density for the normalizingsegment sequence. The calculation is repeated for each of allchromosomes of interest. Steps (a)-(d) can be repeated for test samplesfrom different maternal subjects.

A means for comparing segment doses of different sample sets is providedby determining a normalized segment value (NSV), which relates thesegment dose in a test sample to the mean of the of the correspondingsegment dose in a set of qualified samples. The NSV is calculated as:

${NSV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thsegment dose in a set of qualified samples, and x_(ij) is the observedj-th segment dose for test sample i.

In some embodiments, the presence or absence of one partial fetalchromosomal aneuploidy is determined. In other embodiments, the presenceor absence of two, three, four, five, six, seven, eight, nine, ten,fifteen, twenty, twenty-five, or more partial fetal chromosomalaneuplodies are determined in a sample. In one embodiment, one segmentof interest selected from any one of chromosomes 1-22, X, and Y isselected from chromosomes 1-22, X, and Y. In another embodiment, two ormore segments of interest selected from chromosomes 1-22, X, and Y areselected from any two or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In oneembodiment, any one or more segments of interest are selected fromchromosomes 1-22, X, and Y comprise at least one, five, ten, 15, 20, 25or more segments selected from chromosomes 1-22, X, and Y, and whereinthe presence or absence of at least one, five, ten, 15, 20, 25 differentpartial fetal chromosomal aneuploidies is determined. Different partialfetal chromosomal aneuploidies that can be determined include fetalchromosomal aneuploidies include partial duplications, partialmultiplications, partial insertions and partial deletions. Examples ofpartial fetal chromosomal aneuploidies include partial monosomies andpartial trisomies of autosomes. Partial monosomies of autosomes includepartial monosomy of chromosome 1, partial monosomy of chromosome 4,partial monosomy of chromosome 5, partial monosomy of chromosome 7,partial monosomy of chromosome 11, partial monosomy of chromosome 15,partial monosomy of chromosome 17, partial monosomy of chromosome 18,and partial monosomy of chromosome 22. Other partial fetal chromosomalaneuploidies that can be determined according to the present method aredescribed below.

In any one of the embodiments described above, the test sample is amaternal sample selected from blood, plasma, serum, urine and salivasamples. In some embodiments, the maternal test sample is a plasmasample. The nucleic acid molecules of the maternal sample are a mixtureof fetal and maternal cell-free DNA molecules. Sequencing of the nucleicacids can be performed using next generation sequencing (NGS) asdescribed elsewhere herein. In some embodiments, sequencing is massivelyparallel sequencing using sequencing-by-synthesis with reversible dyeterminators. In other embodiments, sequencing is sequencing-by-ligation.In yet other embodiments, sequencing is single molecule sequencing.Optionally, an amplification step is performed prior to sequencing.

Determination of CNV of Clinical Disorders

In addition to the early determination of birth defects, the methodsdescribed herein can be applied to the determination of any abnormalityin the representation of genetic sequences within the genome. A numberof abnormalities in the representation of genetic sequences within thegenome have been associated with various pathologies. Such pathologiesinclude, but are not limited to cancer, infectious and autoimmunediseases, diseases of the nervous system, metabolic and/orcardiovascular diseases, and the like.

Accordingly in various embodiments use of the methods described hereinin the diagnosis, and/or monitoring, and or treating such pathologies iscontemplated. For example, the methods can be applied to determining thepresence or absence of a disease, to monitoring the progression of adisease and/or the efficacy of a treatment regimen, to determining thepresence or absence of nucleic acids of a pathogen e.g. virus; todetermining chromosomal abnormalities associated with graft versus hostdisease (GVHD), and to determining the contribution of individuals inforensic analyses.

CNVs in Cancer

It has been shown that blood plasma and serum DNA from cancer patientscontains measurable quantities of tumor DNA, that can be recovered andused as surrogate source of tumor DNA, and tumors are characterized byaneuploidy, or inappropriate numbers of gene sequences or even entirechromosomes. The determination of a difference in the amount of a givensequence i.e. a sequence of interest, in a sample from an individual canthus be used in the prognosis or diagnosis of a medical condition. Insome embodiments, the present method can be used to determine thepresence or absence of a chromosomal aneuploidy in a patient suspectedor known to be suffering from cancer.

In certain embodiments the aneuploidy is characteristic of the genome ofthe subject and results in a generally increased predisposition to acancer. In certain embodiments the aneuploidy is characteristic ofparticular cells (e.g., tumor cells, proto-tumor neoplastic cells, etc.)that are or have an increased predisposition to neoplasia. Particularaneuploidies are associated with particular cancers or predispositionsto particular cancers as described below.

Accordingly, various embodiments of the methods described herein providea determination of copy number variation of sequence(s) of interest e.g.clinically-relevant sequence(s), in a test sample from a subject wherecertain variations in copy number provide an indicator of the presenceand/or a predisposition to a cancer. In certain embodiments the samplecomprises a mixture of nucleic acids is derived from two or more typesof cells. In one embodiment, the mixture of nucleic acids is derivedfrom normal and cancerous cells derived from a subject suffering from amedical condition e.g. cancer.

The development of cancer is often accompanied by an alteration innumber of whole chromosomes i.e. complete chromosomal aneuploidy, and/oran alteration in the number of segments of chromosomes i.e. partialaneuploidy, caused by a process known as chromosome instability (CIN)(Thoma et al., Swiss Med Weekly 2011:141:w 13170). It is believed thatmany solid tumors, such as breast cancer, progress from initiation tometastasis through the accumulation of several genetic aberrations.[Sato et al., Cancer Res., 50: 7184-7189 [1990]; Jongsma et al., J ClinPathol: Mol Path 55:305-309 [2002])]. Such genetic aberrations, as theyaccumulate, may confer proliferative advantages, genetic instability andthe attendant ability to evolve drug resistance rapidly, and enhancedangiogenesis, proteolysis and metastasis. The genetic aberrations mayaffect either recessive “tumor suppressor genes” or dominantly actingoncogenes. Deletions and recombination leading to loss of heterozygosity(LOH) are believed to play a major role in tumor progression byuncovering mutated tumor suppressor alleles.

cfDNA has been found in the circulation of patients diagnosed withmalignancies including but not limited to lung cancer (Pathak et al.Clin Chem 52:1833-1842 [2006]), prostate cancer (Schwartzenbach et al.Clin Cancer Res 15:1032-8 [2009]), and breast cancer (Schwartzenbach etal. available online at breast-cancer-research.com/content/11/5/R71[2009]). Identification of genomic instabilities associated with cancersthat can be determined in the circulating cfDNA in cancer patients is apotential diagnostic and prognostic tool. In one embodiment, methodsdescribed herein are used to determine CNV of one or more sequence(s) ofinterest in a sample, e.g., a sample comprising a mixture of nucleicacids derived from a subject that is suspected or is known to havecancer e.g. carcinoma, sarcoma, lymphoma, leukemia, germ cell tumors andblastoma. In one embodiment, the sample is a plasma sample derived(processed) from peripheral blood that may comprise a mixture of cfDNAderived from normal and cancerous cells. In another embodiment, thebiological sample that is needed to determine whether a CNV is presentis derived from a cells that, if a cancer is present, comprise a mixtureof cancerous and non-cancerous cells from other biological tissuesincluding, but not limited to biological fluids such as serum, sweat,tears, sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinalfluid, ravages, bone marrow suspension, vaginal flow, transcervicallavage, brain fluid, ascites, milk, secretions of the respiratory,intestinal and genitourinary tracts, and leukophoresis samples, or intissue biopsies, swabs, or smears. In other embodiments, the biologicalsample is a stool (fecal) sample.

The methods described herein are not limited to the analysis of cfDNA.It will be recognized that similar analyses can be performed on cellularDNA samples.

In various embodiments the sequence(s) of interest comprise nucleic acidsequence(s) known or is suspected to play a role in the developmentand/or progression of the cancer. Examples of a sequence of interestinclude nucleic acids sequences e.g. complete chromosomes and/orsegments of chromosomes, that are amplified or deleted in cancerouscells as described below.

Total CNV Number and Risk for Cancer.

Common cancer SNPs—and by analogy common cancer CNVs may each conferonly a minor increase in disease risk. However, collectively they maycause a substantially elevated risk for cancers. In this regard it isnoted that germline gains and losses of large DNA segments have beenreported as factors predisposing individuals to neuroblastoma, prostateand colorectal cancer, breast cancer, and BRCA1-associated ovariancancer (see, e.g., Krepischi et al. Breast Cancer Res., 14: R24 [2012];Diskin et al. Nature 2009, 459:987-991; Liu et al. Cancer Res 2009, 69:2176-2179; Lucito et al. Cancer Biol Ther 2007, 6:1592-1599; Thean etal. Genes Chromosomes Cancer 2010, 49:99-106; Venkatachalam et al. Int JCancer 2011, 129:1635-1642; and Yoshihara et al. Genes ChromosomesCancer 2011, 50:167-177). It is noted that CNVs frequently found in thehealthy population (common CNVs) are believed to have a role in canceretiology (see, e.g., Shlien and Malkin (2009) Genome Medicine, 1(6):62). In one study testing the hypothesis that common CNVs are associatedwith malignancy (Shlien et al. Proc Natl Acad Sci USA 2008,105:11264-11269) a map of every known CNV whose locus coincides withthat of bona fide cancer-related genes (as catalogued by Higgins et al.Nucleic Acids Res 2007, 35:D721-726) was created. These were termed“cancer CNVs”. In an initial analysis (Shlien et al. Proc Natl Acad SciUSA 2008, 105:11264-11269), 770 healthy genomes were evaluated using theAffymetrix 500K array set, which has an average inter-probe distance of5.8 kb. As CNVs are generally thought to be depleted in gene regions(Redon et al. (2006) Nature 2006, 444:444-454), it was surprising tofind 49 cancer genes that were directly encompassed or overlapped by aCNV in more than one person in a large reference population. In the topten genes, cancer CNVs could be found in four or more people.

It is thus believed that CNV frequency can be used as a measure of riskfor cancer (see, e.g., U.S. Patent Publication No: 2010/0261183 A1). TheCNV frequency can be determined simply by the constitutive genome of theorganism or it can represent a fraction derived from one or more tumors(neoplastic cells) if such are present.

In certain embodiments a number of CNVs in a test sample (e.g., a samplecomprising a constitutional (germline) nucleic acid) or a mixture ofnucleic acids (e.g., a germline nucleic acid and nucleic acid(s) derivedfrom neoplastic cells) is determined using the methods described hereinfor copy number variations. Identification of an increased number ofCNVs in the test sample, e.g., in comparison to a reference value isindicative of a risk of or pre-disposition for cancer in the subject. Itwill be appreciated that the reference value may vary with a givenpopulation. It will also be appreciated that the absolute value of theincrease in CNV frequency will vary depending on the resolution of themethod utilized to determine CNV frequency and other parameters.Typically, an increase in CNV frequency of at least about 1.2 times thereference value been determined to indicative of risk for cancer (see,e.g., U.S. Patent Publication No: 2010/0261183 A1), for example anincrease in CNV frequency of at least or about 1.5 times the referencevalue or greater, such as 2-4 times the reference value is an indicatorof an increased risk of cancer (e.g., as compared to the normal healthyreference population).

A determination of structural variation in the genome of a mammal incomparison to a reference value is also believed to be indicative ofrisk of cancer. In this context, in one embodiment, the term “structuralvariation” is can be defined as the CNV frequency in a mammal multipliedby the average CNV size (in bp) in the mammal. Thus, high structuralvariation scores will result due to increased CNV frequency and/or dueto the occurrence of large genomic nucleic acid deletions orduplications. Accordingly, in certain embodiments a number of CNVs in atest sample (e.g., a sample comprising a constitutional (germline)nucleic acid) is determined using the methods described herein todetermine size and number of copy number variations. In certainembodiments a total structural variation score within genomic DNA ofgreater than about 1 megabase, or greater than about 1.1 megabases, orgreater than about 1.2 megabases, or greater than about 1.3 megabases,or greater than about 1.4 megabases, or greater than about 1.5megabases, or greater than about 1.8 megabases, or greater than about 2megabases of DNA is indicative of risk of cancer.

It is believed these methods provide a measure of the risk of any cancerincluding but not limited to, acute and chronic leukemias, lymphomas,numerous solid tumors of mesenchymal or epithelial tissue, brain,breast, liver, stomach, colon cancer, B cell lymphoma, lung cancer, abronchus cancer, a colorectal cancer, a prostate cancer, a breastcancer, a pancreas cancer, a stomach cancer, an ovarian cancer, aurinary bladder cancer, a brain or central nervous system cancer, aperipheral nervous system cancer, an esophageal cancer, a cervicalcancer, a melanoma, a uterine or endometrial cancer, a cancer of theoral cavity or pharynx, a liver cancer, a kidney cancer, a biliary tractcancer, a small bowel or appendix cancer, a salivary gland cancer, athyroid gland cancer, a adrenal gland cancer, an osteosarcoma, achondrosarcoma, a liposarcoma, a testes cancer, and a malignant fibroushistiocytoma, and other cancers.

Full Chromosome Aneuploidies.

As indicated above, there exists a high frequency of aneuploidy incancer. In certain studies examining the prevalence of somatic copynumber alterations (SCNAs) in cancer, it has been discovered thatone-quarter of the genome of a typical cancer cell is affected either bywhole-arm SCNAs or by the whole-chromosome SCNAs of aneuploidy (see,e.g., Beroukhim et al. Nature 463: 899-905 [2010]). Whole-chromosomealterations are recurrently observed in several cancer types. Forexample, the gain of chromosome 8 is seen in 10-20% of cases of acutemyeloid leukaemia (AML), as well as some solid tumours, includingEwing's Sarcoma and desmoid tumours (see, e.g., Barnard et al. Leukemia10: 5-12 [1996]; Maurici et al. Cancer Genet. Cytogenet. 100: 106-110[1998]; Qi et al. Cancer Genet. Cytogenet. 92: 147-149 [1996]; Barnard,D. R. et al. Blood 100: 427-434 [2002]; and the like. Illustrative, butnon-limiting list of chromosome gains and losses in human cancers areshown in Table 1.

TABLE 1 Illustrative specific, recurrent chromosome gains and losses inhuman cancer (see, e.g., Gordon et al. (2012) Nature Rev. Genetics, 13:189-203). Gains Losses Chromosome Cancer Type Cancer Type 1 Multiplemyeloma Adenocarcinoma (kidney) Adenocarcinoma (breast) 2 HepatoblastomaEwing's sarcoma 3 Multiple myeloma Melanoma Diffuse large B-celllymphoma Adenocarcinoma (kidney) 4 Acute lymphoblastic leukaemiaAdenocarcinoma (kidney) 5 Multiple myeloma Adenocarcinoma (kidney) 6Acute lymphoblastic leukaemia Adenocarcinoma (kidney) Wilms' tumour 7Adenocarcinoma (kidney) Acute myeloid leukaemia Adenocarcinoma(intestine) Juvenile myelomonocytic leukaemia 8 Acute myeloid leukaemiaAdenocarcinoma (kidney) Chronic myeloid leukaemia Ewing's sarcoma 9Multiple myeloma Polycythaemia vera 10 Acute lymphoblastic leukaemiaAstrocytoma Adenocarcinoma (uterus) Multiple myeloma 11 Multiple myeloma12 Chronic lymphocytic leukaemia Multiple myeloma Wilms' tumor 13 Acutemyeloid leukaemia Multiple myeloma Wilms' tumor 14 Acute lymphoblasticleukaemia Adenocarcinoma (kidney) Meningioma 15 Multiple myeloma 16Adenocarcinoma (kidney) Multiple myeloma 17 Adenocarcinoma (kidney)Acute lymphoblastic leukaemia 18 Acute lymphoblastic leukaemiaAdenocarcinoma (kidney) Wilms' tumour 19 Multiple myeloma Adenocarcinoma(Breast) Chronic myeloid leukaemia Meningioma 20 HepatoblastomaAdenocarcinoma (kidney) 21 Acute lymphoblastic leukaemia Acutemegakaryoblastic leukaemia 22 Acute lymphoblastic leukaemia Meningioma XAcute lymphoblastic leukaemia Follicular lymphoma YIn various embodiments, the methods described herein can be used todetect and/or quantify whole chromosome aneuploidies that are associatedwith cancer generally, and/or that are associated with particularcancers. Thus, for example, in certain embodiments, detection and/orquantification of whole chromosome aneuploidies characterized by thegains or losses shown in Table 1 are contemplated.

Arm Level Chromosomal Segment Copy Number Variations.

Multiple studies have reported patterns of arm-level copy numbervariations across large numbers of cancer specimens (Lin et al. CancerRes 68, 664-673 (2008); George et al. PLoS ONE 2, e255 (2007);Demichelis et al. Genes Chromosomes Cancer 48: 366-380 (2009); Beroukhimet al. Nature. 463(7283): 899-905 [2010]). It has additionally beenobserved that the frequency of arm-level copy number variationsdecreases with the length of chromosome arms. Adjusted for this trend,the majority of chromosome arms exhibit strong evidence of preferentialgain or loss, but rarely both, across multiple cancer lineages (see,e.g., Beroukhim et al. Nature. 463(7283): 899-905 [2010]).

Accordingly, in one embodiment, methods described herein are used todetermine arm level CNVs (CNVs comprising one chromosomal arm orsubstantially one chromosomal arm) in a sample. The CNVs can bedetermined in a CNVs in a test sample comprising a constitutional(germline) nucleic acid and the arm level CNVs can be identified inthose constitutional nucleic acids. In certain embodiments arm levelCNVs are identified (if present) in a sample comprising a mixture ofnucleic acids (e.g., nucleic acids derived from normal and nucleic acidsderived from neoplastic cells). In certain embodiments the sample isderived from a subject that is suspected or is known to have cancer e.g.carcinoma, sarcoma, lymphoma, leukemia, germ cell tumors, blastoma, andthe like. In one embodiment, the sample is a plasma sample derived(processed) from peripheral blood that may comprise a mixture of cfDNAderived from normal and cancerous cells. In another embodiment, thebiological sample that is used to determine whether a CNV is present isderived from a cells that, if a cancer is present, comprise a mixture ofcancerous and non-cancerous cells from other biological tissuesincluding, but not limited to biological fluids such as serum, sweat,tears, sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinalfluid, ravages, bone marrow suspension, vaginal flow, transcervicallavage, brain fluid, ascites, milk, secretions of the respiratory,intestinal and genitourinary tracts, and leukophoresis samples, or intissue biopsies, swabs, or smears. In other embodiments, the biologicalsample is a stool (fecal) sample.

In various embodiments the CNVs identified as indicative of the presenceof a cancer or an increased risk for a cancer include, but are notlimited to the arm level CNVs listed in Table 2. As illustrated in Table2 certain CNVs that comprise a substantial arm-level gain are indicativeof the presence of a cancer or an increased risk for a certain cancers.Thus, for example, a gain in 1q is indicative of the presence orincreased risk for acute lymphoblastic leukemia (ALL), breast cancer,GIST, HCC, lung NSC, medulloblastoma, melanoma, MPD, ovarian cancer,and/or prostate cancer. A gain in 3q is indicative of the presence orincreased risk for Esophageal Squamous cancer, Lung SC, and/or MPD. Again in 7q is indicative of the presence or increased risk forcolorectal cancer, glioma, HCC, lung NSC, medulloblastoma, melanoma,prostate cancer, and/or renal cancer. A gain in 7p is indicative of thepresence or increased risk for breast cancer, colorectal cancer,esophageal adenocarcinoma, glioma, HCC, Lung NSC, medulloblastoma,melanoma, and/or renal cancer. A gain in 20q is indicative of thepresence or increased risk for breast cancer, colorectal cancer,dedifferentiated liposarcoma, esophageal adenocarcinoma, esophagealsquamous, glioma cancer, HCC, lung NSC, melanoma, ovarian cancer, and/orrenal cancer, and so forth.

Similarly as illustrated in Table 2 certain CNVs that comprise asubstantial arm-level loss are indicative of the presence of and/or anincreased risk for certain cancers. Thus, for example, a loss in 1p isindicative of the presence or increased risk for gastrointestinalstromal tumor. A loss in 4q is indicative of the presence or increasedrisk for colorectal cancer, esophageal adenocarcinoma, lung sc,melanoma, ovarian cancer, and/or renal cancer. a loss in 17p isindicative of the presence or increased risk for breast cancer,colorectal cancer, esophageal adenocarcinoma, HCC, lung NSC, lung SC,and/or ovarian cancer, and the like.

TABLE 2 Significant arm-level chromosomal segment copy numberalterations in each of 16 cancer subtypes (breast, colorectal,dedifferentiated liposarcoma, esophageal adenocarcinoma, esophagealsquamous, GIST (gastrointestinal stromal tumor), glioma, HCC(hepatocellular carcinoma), lung NSC, lung SC, medulloblastoma,melanoma, MPD (myeloproliferative disease), ovarian, prostate, acutelymphoblastic leukemia (ALL), and renal) (see, e.g., Beroukhim et al.Nature (2010) 463(7283): 899-905). Known Cancer Types Cancer TypesOncogene/Tumor Arm Significantly Gained In Significantly Lost InSuppressor Gene  1p — GIST  1q ALL, Breast, GIST, HCC, Lung — NSC,Medulloblastoma, Melanoma, MPD, Ovarian, Prostate  3p — EsophagealSquamous, Lung VHL NSC, Lung SC, Renal  3q Esophageal Squamous, Lung SC,— MPD  4p ALL Breast, Esophageal Adenocarcinoma, Renal  4q ALLColorectal, Esophageal Adenocarcinoma, Lung SC, Melanoma, Ovarian, Renal 5p Esophageal Squamous, HCC, — TERT Lung NSC, Lung SC, Renal  5q HCC,Renal Esophageal Adenocarcinoma, APC Lung NSC  6p ALL, HCC, Lung NSC, —Melanoma  6q ALL Melanoma, Renal  7p Breast, Colorectal, Esophageal —EGFR Adenocarcinoma, Glioma, HCC, Lung NSC, Medulloblastoma, Melanoma,Renal  7q Colorectal, Glioma, HCC, Lung — BRAF, MET NSC,Medulloblastoma, Melanoma, Prostate, Renal  8p ALL, MPD Breast, HCC,Lung NSC, Medulloblastoma, Prostate, Renal  8q ALL, Breast, Colorectal,Medulloblastoma MYC Esophageal Adenocarcinoma, Esophageal Squamous, HCC,Lung NSC, MPD, Ovarian, Prostate  9p MPD ALL, Breast, EsophagealCDKN2A/B Adenocarcinoma, Lung NSC, Melanoma, Ovarian, Renal  9q ALL, MPDLung NSC, Melanoma, Ovarian, Renal 10p ALL Glioma, Lung SC, Melanoma 10qALL Glioma, Lung SC, PTEN Medulloblastoma, Melanoma 11p —Medulloblastoma WT1 11q — Dedifferentiated Liposarcoma, ATMMedulloblastoma, Melanoma 12p Colorectal, Renal — KRAS 12q Renal — 13qColorectal Breast, Dedifferentiated RB1/BRCA2 Liposarcoma, Glioma, LungNSC, Ovarian 14q ALL, Lung NSC, Lung SC, GIST, Melanoma, Renal Prostate15q — GIST, Lung NSC, Lung SC, Ovarian 16p Breast — 16q — Breast, HCC,Medulloblastoma, Ovarian, Prostate 17p ALL Breast, Colorectal,Esophageal TP53 Adenocarcinoma, HCC, Lung NSC, Lung SC, Ovarian 17q ALL,HCC, Lung NSC, Breast, Ovarian ERBB2, Medulloblastoma NF1/BRCA1 18p ALL,Medulloblastoma Colorectal, Lung NSC 18q ALL, MedulloblastomaColorectal, Esophageal SMAD2, SMAD4 Adenocarcinoma, Lung NSC 19p GliomaEsophageal Adenocarcinoma, Lung NSC, Melanoma, Ovarian 19q Glioma, LungSC Esophageal Adenocarcinoma, Lung NSC 20p Breast, Colorectal,Esophageal — Adenocarcinoma, Esophageal Squamous, GIST, Glioma, HCC,Lung NSC, Melanoma, Renal 20q Breast, Colorectal, — DedifferentiatedLiposarcoma, Esophageal Adenocarcinoma, Esophageal Squamous, Glioma,HCC, Lung NSC, Melanoma, Ovarian, Renal 21q ALL, GIST, MPD — 22qMelanoma Breast, Colorectal, NF2 Dedifferentiated Liposarcoma,Esophageal Adenocarcinoma, GIST, Lung NSC, Lung SC, Ovarian, Prostate

The examples of associations between arm level copy number variationsare intended to be illustrative and not limiting. Other arm level copynumber variations and their cancer associations are known to those ofskill in the art.

Smaller. e.g., Focal, Copy Number Variations.

As indicated above, in certain embodiments, the methods described hereincan be used to determine the presence or absence of a chromosomalamplification. In some embodiments, the chromosomal amplification is thegain of one or more entire chromosomes. In other embodiments, thechromosomal amplification is the gain of one or more segments of achromosome. In yet other embodiments, the chromosomal amplification isthe gain of two or more segments of two or more chromosomes. In variousembodiments, the chromosomal amplification can involve the gain of oneor more oncogenes.

Dominantly acting genes associated with human solid tumors typicallyexert their effect by overexpression or altered expression. Geneamplification is a common mechanism leading to upregulation of geneexpression. Evidence from cytogenetic studies indicates that significantamplification occurs in over 50% of human breast cancers. Most notably,the amplification of the proto-oncogene human epidermal growth factorreceptor 2 (HER2) located on chromosome 17 (17(17q21-q22)), results inoverexpression of HER2 receptors on the cell surface leading toexcessive and dysregulated signaling in breast cancer and othermalignancies (Park et al., Clinical Breast Cancer 8:392-401 [2008]). Avariety of oncogenes have been found to be amplified in other humanmalignancies. Examples of the amplification of cellular oncogenes inhuman tumors include amplifications of: c-myc in promyelocytic leukemiacell line HL60, and in small-cell lung carcinoma cell lines, N-myc inprimary neuroblastomas (stages III and IV), neuroblastoma cell lines,retinoblastoma cell line and primary tumors, and small-cell lungcarcinoma lines and tumors, L-myc in small-cell lung carcinoma celllines and tumors, c-myb in acute myeloid leukemia and in colon carcinomacell lines, c-erbb in epidermoid carcinoma cell, and primary gliomas,c-K-ras-2 in primary carcinomas of lung, colon, bladder, and rectum,N-ras in mammary carcinoma cell line (Varmus H., Ann Rev Genetics 18:553-612 (1984) [cited in Watson et al., Molecular Biology of the Gene(4th ed.; Benjamin/Cummings Publishing Co. 1987)].

Duplications of oncogenes are a common cause of many types of cancer, asis the case with P70-S6 Kinase 1 amplification and breast cancer. Insuch cases the genetic duplication occurs in a somatic cell and affectsonly the genome of the cancer cells themselves, not the entire organism,much less any subsequent offspring. Other examples of oncogenes that areamplified in human cancers include MYC, ERBB2 (EFGR), CCND1 (Cyclin D1),FGFR1 and FGFR2 in breast cancer, MYC and ERBB2 in cervical cancer,HRAS, KRAS, and MYB in colorectal cancer, MYC, CCND1 and MDM2 inesophageal cancer, CCNE, KRAS and MET in gastric cancer, ERBB1, and CDK4in glioblastoma, CCND1, ERBB1, and MYC in head and neck cancer, CCND1 inhepatocellular cancer, MYCB in neuroblastoma, MYC, ERBB2 and AKT2 inovarian cancer, MDM2 and CDK4 in sarcoma, and MYC in small cell lungcancer. In one embodiment, the present method can be used to determinethe presence or absence of amplification of an oncogene associated witha cancer. In some embodiments, the amplified oncogene is associated withbreast cancer, cervical cancer, colorectal cancer, esophageal cancer,gastric cancer, glioblastoma, head and neck cancer, hepatocellularcancer, neuroblastoma, ovarian cancer, sarcoma, and small cell lungcancer.

In one embodiment, the present method can be used to determine thepresence or absence of a chromosomal deletion. In some embodiments, thechromosomal deletion is the loss of one or more entire chromosomes. Inother embodiments, the chromosomal deletion is the loss of one or moresegments of a chromosome. In yet other embodiments, the chromosomaldeletion is the loss of two or more segments of two or more chromosomes.The chromosomal deletion can involve the loss of one or more tumorsuppressor genes.

Chromosomal deletions involving tumor suppressor genes are believed toplay an important role in the development and progression of solidtumors. The retinoblastoma tumor suppressor gene (Rb-1), located inchromosome 13q14, is the most extensively characterized tumor suppressorgene. The Rb-1 gene product, a 105 kDa nuclear phosphoprotein,apparently plays an important role in cell cycle regulation (Howe etal., Proc Natl Acad Sci (USA) 87:5883-5887 [1990]). Altered or lostexpression of the Rb protein is caused by inactivation of both genealleles either through a point mutation or a chromosomal deletion. Rb-igene alterations have been found to be present not only inretinoblastomas but also in other malignancies such as osteosarcomas,small cell lung cancer (Rygaard et al., Cancer Res 50: 5312-5317[1990)]) and breast cancer. Restriction fragment length polymorphism(RFLP) studies have indicated that such tumor types have frequently lostheterozygosity at 13q suggesting that one of the Rb-1 gene alleles hasbeen lost due to a gross chromosomal deletion (Bowcock et al., Am J HumGenet, 46: 12 [1990]). Chromosome 1 abnormalities includingduplications, deletions and unbalanced translocations involvingchromosome 6 and other partner chromosomes indicate that regions ofchromosome 1, in particular 1q21-1q32 and 1p11-13, might harboroncogenes or tumor suppressor genes that are pathogenetically relevantto both chronic and advanced phases of myeloproliferative neoplasms(Caramazza et al., Eur J Hematol 84:191-200 [2010]). Myeloproliferativeneoplasms are also associated with deletions of chromosome 5. Completeloss or interstitial deletions of chromosome 5 are the most commonkaryotypic abnormality in myelodysplastic syndromes (MDSs). Isolateddel(5q)/5q-MDS patients have a more favorable prognosis than those withadditional karyotypic defects, who tend to develop myeloproliferativeneoplasms (MPNs) and acute myeloid leukemia. The frequency of unbalancedchromosome 5 deletions has led to the idea that 5q harbors one or moretumor-suppressor genes that have fundamental roles in the growth controlof hematopoietic stem/progenitor cells (HSCs/HPCs). Cytogenetic mappingof commonly deleted regions (CDRs) centered on 5q31 and 5q32 identifiedcandidate tumor-suppressor genes, including the ribosomal subunit RPS14, the transcription factor Egrl/Krox20 and the cytoskeletal remodelingprotein, alpha-catenin (Eisenmann et al., Oncogene 28:3429-3441 [2009]).Cytogenetic and allelotyping studies of fresh tumors and tumor celllines have shown that allelic loss from several distinct regions onchromosome 3p, including 3p25, 3p21-22, 3p21.3, 3p12-13 and 3p14, arethe earliest and most frequent genomic abnormalities involved in a widespectrum of major epithelial cancers of lung, breast, kidney, head andneck, ovary, cervix, colon, pancreas, esophagus, bladder and otherorgans. Several tumor suppressor genes have been mapped to thechromosome 3p region, and are thought that interstitial deletions orpromoter hypermethylation precede the loss of the 3p or the entirechromosome 3 in the development of carcinomas (Angeloni D., BriefingsFunctional Genomics 6:19-39 [2007]).

Newborns and children with Down syndrome (DS) often present withcongenital transient leukemia and have an increased risk of acutemyeloid leukemia and acute lymphoblastic leukemia. Chromosome 21,harboring about 300 genes, may be involved in numerous structuralaberrations, e.g., translocations, deletions, and amplifications, inleukemias, lymphomas, and solid tumors. Moreover, genes located onchromosome 21 have been identified that play an important role intumorigenesis. Somatic numerical as well as structural chromosome 21aberrations are associated with leukemias, and specific genes includingRUNX 1, TMPRSS2, and TFF, which are located in 21q, play a role intumorigenesis (Fonatsch C Gene Chromosomes Cancer 49:497-508 [2010]).

In view of the foregoing, in various embodiments the methods describedherein can be used to determine the segment CNVs that are known tocomprise one or more oncogenes or tumor suppressor genes, and/or thatare known to be associated with a cancer or an increased risk of cancer.In certain embodiments, the CNVs can be determined in a test samplecomprising a constitutional (germline) nucleic acid and the segment canbe identified in those constitutional nucleic acids. In certainembodiments segment CNVs are identified (if present) in a samplecomprising a mixture of nucleic acids (e.g., nucleic acids derived fromnormal and nucleic acids derived from neoplastic cells). In certainembodiments the sample is derived from a subject that is suspected or isknown to have cancer e.g. carcinoma, sarcoma, lymphoma, leukemia, germcell tumors, blastoma, and the like. In one embodiment, the sample is aplasma sample derived (processed) from peripheral blood that maycomprise a mixture of cfDNA derived from normal and cancerous cells. Inanother embodiment, the biological sample that is used to determinewhether a CNV is present is derived from a cells that, if a cancer ispresent, comprises a mixture of cancerous and non-cancerous cells fromother biological tissues including, but not limited to biological fluidssuch as serum, sweat, tears, sputum, urine, sputum, ear flow, lymph,saliva, cerebrospinal fluid, ravages, bone marrow suspension, vaginalflow, transcervical lavage, brain fluid, ascites, milk, secretions ofthe respiratory, intestinal and genitourinary tracts, and leukophoresissamples, or in tissue biopsies, swabs, or smears. In other embodiments,the biological sample is a stool (fecal) sample.

The CNVs used to determine presence of a cancer and/or increased riskfor a cancer can comprise amplification or deletions.

In various embodiments the CNVs identified as indicative of the presenceof a cancer or an increased risk for a cancer include one or more of theamplifications shown in Table 3.

TABLE 3 Illustrative, but non-limiting chromosomal segmentscharacterized by amplifications that are associated with cancers. Cancertypes listed are those identified in Beroukhim et al. Nature 18: 463:899-905. Length Cancer types identified in this Peak region (Mb)analysis but not prior publications chr1: 119996566- 0.228 Breast, LungSC, Melanoma 120303234 chr1: 148661965- 0.35 Breast, Dedifferentiatedliposarcoma, 149063439 Esophageal adenocarcinoma, Hepatocellular, LungSC, Melanoma, Ovarian, Prostate, Renal chr1: 1-5160566 4.416 Esophagealadenocarcinoma, Ovarian chr1: 158317017- 1.627 Dedifferentiatedliposarcoma, Esophageal 159953843 adenocarcinoma, Prostate, Renal chr1:169549478- 0.889 Colorectal, Dedifferentiated liposarcoma, 170484405Prostate, Renal chr1: 201678483- 1.471 Prostate 203358272 chr1:241364021- 5.678 Lung NSC, Melanoma, Ovarian 247249719 chr1: 39907605-0.319 Acute lymphoblastic leukemia, Breast, 40263248 Lung NSC, Lung SCchr1: 58658784- 1.544 Breast, Dedifferentiated liposarcoma, 60221344Lung SC chr3: 170024984- 3.496 Breast, Esophageal adenocarcinoma,173604597 Glioma chr3: 178149984- 21.123 Esophageal squamous, Lung NSC199501827 chr3: 86250885- 8.795 Lung SC, Melanoma 95164178 chr4:54471680- 1.449 Lung NSC 55980061 chr5: 1212750- 0.115 Dedifferentiatedliposarcoma 1378766 chr5: 174477192- 6.124 Breast, Lung NSC 180857866chr5: 45312870- 4.206 Lung SC 49697231 chr6: 1-23628840 23.516Esophageal adenocarcinoma chr6: 135561194- 0.092 Breast, Esophagealadenocarcinoma 135665525 chr6: 43556800- 0.72 Esophageal adenocarcinoma,44361368 Hepatocellular, Ovarian chr6: 63255006- 1.988 Esophagealadenocarcinoma, Lung NSC 65243766 chr7: 115981465- 0.69 Esophagealadenocarcinoma, Lung NSC, 116676953 Melanoma, Ovarian chr7: 54899301-0.363 Esophageal adenocarcinoma, Esophageal 55275419 squamous chr7:89924533- 9.068 Breast, Esophageal adenocarcinoma, 98997268 Esophagealsquamous, Ovarian chr8: 101163387- 2.516 Lung NSC, Melanoma, Ovarian103693879 chr8: 116186189- 4.4 Breast, Hepatocellular, Lung NSC,120600761 Ovarian chr8: 128774432- 0.009 Esophageal adenocarcinoma,Esophageal 128849112 squamous, Hepatocellular, Lung SC, Medulloblastoma,Myeloproliferative disorder, Ovarian chr8: 140458177- 5.784 Lung NSC,Medulloblastoma, Melanoma, 146274826 Ovarian chr8: 38252951- 0.167Colorectal, Esophageal adenocarcinoma, 38460772 Esophageal squamouschr8: 42006632- 0.257 Esophageal adenocarcinoma, Lung NSC, 42404492 LungSC, Ovarian, Prostate chr8: 81242335- 0.717 Breast, Melanoma 81979194chr9: 137859478- 2.29 Colorectal, Dedifferentiated liposarcoma 140273252chr10: 74560456- 7.455 Breast, Ovarian, Prostate 82020637 chr11:101433436- 0.683 Lung NSC, Lung SC 102134907 chr11: 32027116- 5.744Breast, Dedifferentiated liposarcoma, 37799354 Lung NSC, Lung SC chr11:69098089- 0.161 Dedifferentiated liposarcoma, Esophageal 69278404adenocarcinoma, Hepatocellular, Lung SC, Ovarian chr11: 76699529- 1.286Dedifferentiated liposarcoma, Esophageal 78005085 adenocarcinoma, LungSC, Ovarian chr12: 1-1311104 1.271 Lung NSC chr12: 25189655- 0.112 Acutelymphoblastic leukemia, 25352305 Esophageal adenocarcinoma, Esophagealsquamous, Ovarian chr12: 30999223- 1.577 Acute lymphoblastic leukemia,32594050 Colorectal, Esophageal adenocarcinoma, Esophageal squamous,Lung NSC, Lung SC chr12: 38788913- 3.779 Breast, Colorectal,Dedifferentiated 42596599 liposarcoma, Esophageal squamous, Lung NSC,Lung SC chr12: 56419524- 0.021 Dedifferentiated liposarcoma, Melanoma,56488685 Renal chr12: 64461446- 0.041 Dedifferentiated liposarcoma,Renal 64607139 chr12: 66458200- 0.058 Dedifferentiated liposarcoma,Esophageal 66543552 squamous, Renal chr12: 67440273- 0.067 Breast,Dedifferentiated liposarcoma, 67566002 Esophageal squamous, Melanoma,Renal chr12: 68249634- 0.06 Breast, Dedifferentiated liposarcoma,68327233 Esophageal squamous, Renal chr12: 70849987- 0.036Dedifferentiated liposarcoma, Renal 70966467 chr12: 72596017- 0.23 Renal73080626 chr12: 76852527- 0.158 Dedifferentiated liposarcoma 77064746chr12: 85072329- 0.272 Dedifferentiated liposarcoma 85674601 chr12:95089777- 0.161 Dedifferentiated liposarcoma 95350380 chr13: 108477140-1.6 Breast, Esophageal adenocarcinoma, 110084607 Lung NSC, Lung SCchr13: 1-40829685 22.732 Acute lymphoblastic leukemia, Esophagealadenocarcinoma chr13: 89500014- 3.597 Breast, Esophageal adenocarcinoma,93206506 Medulloblastoma chr14: 106074644- 0.203 Esophageal squamous106368585 chr14: 1-23145193 3.635 Acute lymphoblastic leukemia,Esophageal squamous, Hepatocellular, Lung SC chr14: 35708407- 0.383Breast, Esophageal adenocarcinoma, 36097605 Esophageal squamous,Hepatocellular, Prostate chr15: 96891354- 0.778 Breast, Colorectal,Esophageal 97698742 adenocarcinoma, Lung NSC, Medulloblastoma, Melanomachr17: 18837023- 0.815 Breast, Hepatocellular 19933105 chr17: 22479313-0.382 Breast, Lung NSC 22877776 chr17: 24112056- 0.114 Breast, Lung NSC24310787 chr17: 35067383- 0.149 Colorectal, Esophageal adenocarcinoma,35272328 Esophageal squamous chr17: 44673157- 0.351 Melanoma 45060263chr17: 55144989- 0.31 Lung NSC, Medulloblastoma, Melanoma, 55540417Ovarian chr17: 62318152- 1.519 Breast, Lung NSC, Melanoma, Ovarian63890591 chr17: 70767943- 0.537 Breast, Lung NSC, Melanoma, Ovarian71305641 chr18: 17749667- 5.029 Colorectal, Esophageal adenocarcinoma,22797232 Ovarian chr19: 34975531- 0.096 Breast, Esophagealadenocarcinoma, 35098303 Esophageal squamous chr19: 43177306- 2.17 LungNSC, Ovarian 45393020 chr19: 59066340- 0.321 Breast, Lung NSC, Ovarian59471027 chr2: 15977811- 0.056 Lung SC 16073001 chr20: 29526118- 0.246Ovarian 29834552 chr20: 51603033- 0.371 Hepatocellular, Lung NSC,Ovarian 51989829 chr20: 61329497- 0.935 Hepatocellular, Lung NSC62435964 chr22: 19172385- 0.487 Colorectal, Melanoma, Ovarian 19746441chrX: 152729030- 1.748 Breast, Lung NSC, Renal 154913754 chrX: 66436234-0.267 Ovarian, Prostate 67090514

In certain embodiments in combination with the amplifications describedabove (herein), or separately, the CNVs identified as indicative of thepresence of a cancer or an increased risk for a cancer include one ormore of the deletions shown in Table 4.

TABLE 4 Illustrative, but non-limiting chromosomal segmentscharacterized by deletions that are associated with cancers. Cancertypes listed are those identified in Beroukhim et al. Nature 18: 463:899-905. Length Cancer types identified in this Peak region (Mb)analysis but not prior publications chr1: 110339388- 1p13.2 Acutelymphoblastic leukemia, 119426489 Esophageal adenocarcinoma, Lung NSC,Lung SC, Melanoma, Ovarian, Prostate chr1: 223876038- 1q43 Acutelymphoblastic leukemia, Breast, 247249719 Lung SC, Melanoma, Prostatechr1: 26377344- 1p36.11 Breast, Esophageal adenocarcinoma, 27532551Esophageal squamous, Lung NSC, Lung SC, Medulloblastoma,Myeloproliferative disorder, Ovarian, Prostate chr1: 3756302- 1p36.31Acute lymphoblastic leukemia, Breast, 6867390 Esophageal squamous,Hepatocellular, Lung NSC, Lung SC, Medulloblastoma, Myeloproliferativedisorder, Ovarian, Prostate, Renal chr1: 71284749- 1p31.1 Breast,Esophageal adenocarcinoma, 74440273 Glioma, Hepatocellular, Lung NSC,Lung SC, Melanoma, Ovarian, Renal chr2: 1-15244284 2p25.3 Lung NSC,Ovarian chr2: 138479322- 2q22.1 Breast, Colorectal, Esophageal 143365272adenocarcinoma, Esophageal squamous, Hepatocellular, Lung NSC, Ovarian,Prostate, Renal chr2: 204533830- 2q33.2 Esophageal adenocarcinoma,206266883 Hepatocellular, Lung NSC, Medulloblastoma, Renal chr2:241477619- 2q37.3 Breast, Dedifferentiated liposarcoma, 242951149Esophageal adenocarcinoma, Esophageal squamous, Hepatocellular, LungNSC, Lung SC, Medulloblastoma, Melanoma, Ovarian, Renal chr3: 116900556-3q13.31 Dedifferentiated liposarcoma, 120107320 Esophagealadenocarcinoma, Hepatocellular, Lung NSC, Melanoma, Myeloproliferativedisorder, Prostate chr3: 1-2121282 3p26.3 Colorectal, Dedifferentiatedliposarcoma, Esophageal adenocarcinoma, Lung NSC, Melanoma,Myeloproliferative disorder chr3: 175446835- 3q26.31 Acute lymphoblasticleukemia, 178263192 Dedifferentiated liposarcoma, Esophagealadenocarcinoma, Lung NSC, Melanoma, Myeloproliferative disorder,Prostate chr3: 58626894- 3p14.2 Breast, Colorectal, Dedifferentiated61524607 liposarcoma, Esophageal adenocarcinoma, Esophageal squamous,Hepatocellular, Lung NSC, Lung SC, Medulloblastoma, Melanoma,Myeloproliferative disorder, Ovarian, Prostate, Renal chr4: 1-4357934p16.3 Myeloproliferative disorder chr4: 186684565- 4q35.2 Breast,Esophageal adenocarcinoma, 191273063 Esophageal squamous, Lung NSC,Medulloblastoma, Melanoma, Prostate, Renal chr4: 91089383- 4q22.1 Acutelymphoblastic leukemia, Esophageal 93486891 adenocarcinoma,Hepatocellular, Lung NSC, Renal chr5: 177541057- 5q35.3 Breast, LungNSC, Myeloproliferative 180857866 disorder, Ovarian chr5: 57754754-5q11.2 Breast, Colorectal, Dedifferentiated 59053198 liposarcoma,Esophageal adenocarcinoma, Esophageal squamous, Lung SC, Melanoma,Myeloproliferative disorder, Ovarian, Prostate chr5: 85837489- 5q21.1Colorectal, Dedifferentiated liposarcoma, 133480433 Lung NSC, Lung SC,Myeloproliferative disorder, Ovarian chr6: 101000242- 6q22.1 Colorectal,Lung NSC, Lung SC 121511318 chr6: 1543157- 6p25.3 Colorectal,Dedifferentiated liposarcoma, 2570302 Esophageal adenocarcinoma, LungNSC, Lung SC, Ovarian, Prostate chr6: 161612277- 6q26 Colorectal,Esophageal adenocarcinoma, 163134099 Esophageal squamous, Lung NSC, LungSC, Ovarian, Prostate chr6: 76630464- 6q16.1 Colorectal, Hepatocellular,Lung NSC 105342994 chr7: 141592807- 7q34 Breast, Colorectal, Esophageal142264966 adenocarcinoma, Esophageal squamous, Hepatocellular, Lung NSC,Ovarian, Prostate, Renal chr7: 144118814- 7q35 Breast, Esophagealadenocarcinoma, 148066271 Esophageal squamous, Lung NSC, Melanoma,Myeloproliferative disorder, Ovarian chr7: 156893473- 7q36.3 Breast,Esophageal adenocarcinoma, 158821424 Esophageal squamous, Lung NSC,Melanoma, Myeloproliferative disorder, Ovarian, Prostate chr7: 3046420-7p22.2 Melanoma, Myeloproliferative disorder, 4279470 Ovarian chr7:65877239- 7q21.11 Breast, Medulloblastoma, Melanoma, 79629882Myeloproliferative disorder, Ovarian chr8: 1-392555 8p23.3 Acutelymphoblastic leukemia, Breast, Myeloproliferative disorder chr8:2053441- 8p23.2 Acute lymphoblastic leukemia, 6259545 Dedifferentiatedliposarcoma, Esophageal adenocarcinoma, Esophageal squamous,Hepatocellular, Lung NSC, Myeloproliferative disorder chr8: 22125332-8p21.2 Acute lymphoblastic leukemia, 30139123 Dedifferentiatedliposarcoma, Hepatocellular, Myeloproliferative disorder, Ovarian, Renalchr8: 39008109- 8p11.22 Acute lymphoblastic leukemia, Breast, 41238710Dedifferentiated liposarcoma, Esophageal squamous, Hepatocellular, LungNSC, Myeloproliferative disorder, Renal chr8: 42971602- 8q11.22 Breast,Dedifferentiated liposarcoma, 72924037 Esophageal squamous,Hepatocellular, Lung NSC, Myeloproliferative disorder, Renal chr9:1-708871 9p24.3 Acute lymphoblastic leukemia, Breast, Lung NSC,Myeloproliferative disorder, Ovarian, Prostate chr9: 21489625- 9p21.3Colorectal, Esophageal adenocarcinoma, 22474701 Esophageal squamous,Myeloproliferative disorder, Ovarian chr9: 36365710- 9p13.2Myeloproliferative disorder 37139941 chr9: 7161607- 9p24.1 Acutelymphoblastic leukemia, Breast, 12713130 Colorectal, Esophagealadenocarcinoma, Hepatocellular, Lung SC, Medulloblastoma, Melanoma,Myeloproliferative disorder, Ovarian, Prostate, Renal chr10: 1-104294910p15.3 Colorectal, Lung NSC, Lung SC, Ovarian, Prostate, Renal chr10:129812260- 10q26.3 Breast, Colorectal, Glioma, Lung NSC, 135374737 LungSC, Melanoma, Ovarian, Renal chr10: 52313829- 10q11.23 Colorectal, LungNSC, Lung SC, Ovarian, 53768264 Renal chr10: 89467202- 10q23.31 Breast,Lung SC, Ovarian, Renal 90419015 chr11: 107086196- 11q23.1 Esophagealadenocarcinoma, 116175885 Medulloblastoma, Renal chr11: 1-139195411p15.5 Breast, Dedifferentiated liposarcoma, Esophageal adenocarcinoma,Lung NSC, Medulloblastoma, Ovarian chr11: 130280899- 11q25 Esophagealadenocarcinoma, Esophageal 134452384 squamous, Hepatocellular, Lung NSC,Medulloblastoma, Renal chr11: 82612034- 11q14.1 Melanoma, Renal 85091467chr12: 11410696- 12p13.2 Breast, Hepatocellular, Myeloproliferative12118386 disorder, Prostate chr12: 131913408- 12q24.33 Dedifferentiatedliposarcoma, Lung NSC, 132349534 Myeloproliferative disorder chr12:97551177- 12q23.1 Breast, Colorectal, Esophageal squamous, 99047626 LungNSC, Myeloproliferative disorder chr13: 111767404- 13q34 Breast,Hepatocellular, Lung NSC 114142980 chr13: 1-23902184 13q12.11 Breast,Lung SC, Ovarian chr13: 46362859- 13q14.2 Hepatocellular, Lung SC,48209064 Myeloproliferative disorder, Prostate chr13: 92308911- 13q31.3Breast, Hepatocellular, Lung NSC, Renal 94031607 chr14: 1-2914096814q11.2 Acute lymphoblastic leukemia, Esophageal adenocarcinoma,Myeloproliferative disorder chr14: 65275722- 14q23.3 Dedifferentiatedliposarcoma, 67085224 Myeloproliferative disorder chr14: 80741860-14q32.12 Acute lymphoblastic leukemia, 106368585 Dedifferentiatedliposarcoma, Melanoma, Myeloproliferative disorder chr15: 1-2474008415q11.2 Acute lymphoblastic leukemia, Breast, Esophageal adenocarcinoma,Lung NSC, Myeloproliferative disorder, Ovarian chr15: 35140533- 15q15.1Esophageal adenocarcinoma, Lung NSC, 43473382 Myeloproliferativedisorder chr16: 1-359092 16p13.3 Esophageal adenocarcinoma,Hepatocellular, Lung NSC, Renal chr16: 31854743- 16q11.2 Breast,Hepatocellular, Lung NSC, 53525739 Melanoma, Renal chr16: 5062786-16p13.3 Hepatocellular, Lung NSC, 7709383 Medulloblastoma, Melanoma,Myeloproliferative disorder, Ovarian, Renal chr16: 76685816- 16q23.1Breast, Colorectal, Esophageal 78205652 adenocarcinoma, Hepatocellular,Lung NSC, Lung SC, Medulloblastoma, Renal chr16: 80759878- 16q23.3Colorectal, Hepatocellular, Renal 82408573 chr16: 88436931- 16q24.3Colorectal, Hepatocellular, Lung NSC, 88827254 Prostate, Renal chr17:10675416- 17p12 Lung NSC, Lung SC, Myeloproliferative 12635879 disorderchr17: 26185485- 17q11.2 Breast, Colorectal, Dedifferentiated 27216066liposarcoma, Lung NSC, Lung SC, Melanoma, Myeloproliferative disorder,Ovarian chr17: 37319013- 17q21.2 Breast, Colorectal, Dedifferentiated37988602 liposarcoma, Lung SC, Melanoma, Myeloproliferative disorder,Ovarian chr17: 7471230- 17p13.1 Lung SC, Myeloproliferative disorder7717938 chr17: 78087533- 17q25.3 Colorectal, Myeloproliferative disorder78774742 chr18: 1-587750 18p11.32 Myeloproliferative disorder chr18:46172638- 18q21.2 Esophageal adenocarcinoma, Lung NSC 49935241 chr18:75796373- 18q23 Colorectal, Esophageal adenocarcinoma, 76117153Esophageal squamous, Ovarian, Prostate chr19: 1-526082 19p13.3Hepatocellular, Lung NSC, Renal chr19: 21788507- 19p12 Hepatocellular,Lung NSC, Renal 34401877 chr19: 52031294- 19q13.32 Breast,Hepatocellular, Lung NSC, 53331283 Medulloblastoma, Ovarian, Renalchr19: 63402921- 19q13.43 Breast, Colorectal, Dedifferentiated 63811651liposarcoma, Hepatocellular, Lung NSC, Medulloblastoma, Ovarian, Renalchr20: 1-325978 20p13 Breast, Dedifferentiated liposarcoma, Lung NSCchr20: 14210829- 20p12.1 Esophageal adenocarcinoma, Lung NSC, 15988895Medulloblastoma, Melanoma, Myeloproliferative disorder, Prostate, Renalchr21: 38584860- 21q22.2 Breast 42033506 chr22: 20517661- 22q11.22 Acutelymphoblastic leukemia, Esophageal 21169423 adenocarcinoma chr22:45488286- 22q13.33 Breast, Hepatocellular, Lung NSC, Lung 49691432 SCchrX: 1-3243111 Xp22.33 Esophageal adenocarcinoma, Lung NSC, Lung SCchrX: 31041721- Xp21.2 Acute lymphoblastic leukemia, Esophageal 34564697adenocarcinoma, Glioma

The anuploidies identified as characteristic of various cancers (e.g.,the anuploidies identified in Tables 3 and 4) may contain genes known tobe implicated in cancer etiologies (e.g., tumor suppressors, oncogenes,etc.). These aneuploidies can also be probed to identify relevant butpreviously unknown genes.

For example Beroukhim et al. supra, assessed potential cancer-causinggenes in the copy number alterations using GRAIL (Gene RelationshipsAmong Implicated Loci₂₀), an algorithm that searches for functionalrelationships among genomic regions. GRAIL scores each gene in acollection of genomic regions for its ‘relatedness’ to genes in otherregions based on textual similarity between published abstracts for allpapers citing the genes, on the notion that some target genes willfunction in common pathways. These methods permitidentification/characterization of genes previously not associated withthe particular cancers at issue. Table 5 illustrates target genes knownto be within the identified amplified segment and predicted genes, andTable 6 illustrates target genes known to be within the identifieddeleted segment and predicted genes.

TABLE 5 Illustrative, but non-limiting chromosomal segments and genesknown or predicted to be present in regions characterized byamplification in various cancers (see, e.g., Beroukhim et al. supra.).Chromosome Known GRAIL and band Peak region # genes target top target8q24.21 chr8: 128774432- 1 MYC MYC 128849112 11q13.2 chr11: 69098089- 3CCND1 ORAOV1 69278404 17q12 chr17: 35067383- 6 ERBB2 ERBB2, 35272328C17orf37 12q14.1 chr12: 56419524- 7 CDK4 TSPAN31 56488685 14q13.3 chr14:35708407- 3 NKX2-1 NKX2-1 36097605 12q15 chr12: 67440273- 1 MDM2 MDM267566002 7p11.2 chr7: 54899301- 1 EGFR EGFR 55275419 1q21.2 chr1:148661965- 9 MCL1‡ MCL1 149063439 8p12 chr8: 38252951- 3 FGFR1 FGFR138460772 12p12.1 chr12: 25189655- 2 KRAS KRAS 25352305 19q12 chr19:34975531- 1 CCNE1 CCNE1 35098303 22q11.21 chr22: 19172385- 11 CRKL CRKL19746441 12q15 chr12: 68249634- 2 LRRC10 68327233 12q14.3 chr12:64461446- 1 HMGA2 HMGA2 64607139 Xq28 chrX: 152729030- 53 SPRY3154913754 5p15.33 chr5: 1212750- 3 TERT TERT 1378766 3q26.2 chr3:170024984- 22 PRKCI PRKCI 173604597 15q26.3 chr15: 96891354- 4 IGF1RIGF1R 97698742 20q13.2 chr20: 51603033- 1 ZNF217 51989829 8p11.21 chr8:42006632- 6 PLAT 42404492 1p34.2 chr1: 39907605- 7 MYCL1 MYCL1 4026324817q21.33 chr17: 44673157- 4 NGFR, PHB 45060263 2p24.3 chr2: 15977811- 1MYCN MYCN 16073001 7q21.3 chr7: 89924533- 62 CDK6 CDK6 98997268 13q34chr13: 108477140- 4 IRS2 110084607 11q14.1 chr11: 76699529- 14 GAB278005085 20q13.33 chr20: 61329497- 38 BIRC7 62435964 17q23.1 chr17:55144989- 5 RPS6KB1 55540417 1p12 chr1: 119996566- 5 REG4 1203032348q21.13 chr8: 81242335- 3 ZNF704, 81979194 ZBTB10 6p21.1 chr6: 43556800-18 VEGFA 44361368 5p11 chr5: 45312870- 0 49697231 20q11.21 chr20:29526118- 5 BCL2L1‡ BCL2L1, ID1 29834552 6q23.3 chr6: 135561194- 1 MYB**hsa-mir-548a-2 135665525 1q44 chr1: 241364021- 71 AKT3 247249719 5q35.3chr5: 174477192- 92 FLT4 180857866 7q31.2 chr7: 115981465- 3 MET MET116676953 18q11.2 chr18: 17749667- 21 CABLES1 22797232 17q25.1 chr17:70767943- 13 GRB2, ITGB4 71305641 1p32.1 chr1: 58658784- 7 JUN JUN60221344 17q11.2 chr17: 24112056- 5 DHRS13, 24310787 FLOT2, ERAL1, PHF1217p11.2 chr17: 18837023- 12 MAPK7 19933105 8q24.11 chr8: 116186189- 13NOV 120600761 12q15 chr12: 66458200- 0 66543552 19q13.2 chr19: 43177306-60 LGALS7, 45393020 DYRK1B 11q22.2 chr11: 101433436- 8 BIRC2, BIRC2102134907 YAP1 4q12 chr4: 54471680- 7 PDGFRA, KDR, KIT 55980061 KIT12p11.21 chr12: 30999223- 9 DDX11, 32594050 FAM60A 3q28 chr3: 178149984-143 PIK3CA PIK3CA 199501827 1p36.33 chr1: 1-5160566 77 TP73 17q24.2chr17: 62318152- 12 BPTF 63890591 1q23.3 chr1: 158317017- 52 PEA15159953843 1q24.3 chr1: 169549478- 6 BAT2D1, 170484405 MYOC 8q22.3 chr8:101163387- 14 RRM2B 103693879 13q31.3 chr13: 89500014- 3 GPC5 9320650612q21.1 chr12: 70849987- 0 70966467 12p13.33 chr12: 1-1311104 10 WNK112q21.2 chr12: 76852527- 0 77064746 1q32.1 chr1: 201678483- 21 MDM4 MDM4203358272 19q13.42 chr19: 59066340- 19 PRKCG, 59471027 TSEN34 12q12chr12: 38788913- 12 ADAMTS20 42596599 12q23.1 chr12: 95089777- 2 ELK395350380 12q21.32 chr12: 85072329- 0 85674601 10q22.3 chr10: 74560456-46 SFTPA1B 82020637 3p11.1 chr3: 86250885- 8 POU1F1 95164178 17q11.1chr17: 22479313- 1 WSB1 22877776 8q24.3 chr8: 140458177- 97 PTP4A3,146274826 MAFA, PARP10 Xq12 chrX: 66436234- 1 AR AR 67090514 6q12 chr6:63255006- 3 PTP4A1 65243766 14q11.2 chr14: 1-23145193 95 BCL2L2 9q34.3chr9: 137859478- 76 NRARP, 140273252 MRPL41, TRAF2, LHX3 6p24.1 chr6:1-23628840 95 E2F3 13q12.2 chr13: 1-40829685 110 FOXO1 12q21.1 chr12:72596017- 0 73080626 14q32.33 chr14: 106074644- 0 106368585 11p13 chr11:32027116- 35 WT1 37799354

TABLE 6 Illustrative, but non-limiting chromosomal segments and genesknown or predicted to be present in regions charactierzed byamplification in various cancers (see, e.g., Beroukhim et al. supra.).Chromosome # Known GRAIL and band Peak region genes target top target9p21.3 chr9: 21489625- 5 CDKN2A/B CDKN2A 22474701 3p14.2 chr3: 58626894-2 FHIT§ FHIT 61524607 16q23.1 chr16: 76685816- 2 WWOX§ WWOX 782056529p24.1 chr9: 7161607- 3 PTPRD§ PTPRD 12713130 20p12.1 chr20: 14210829- 2MACROD2§ FLRT3 15988895 6q26 chr6: 161612277- 1 PARK2§ PARK2 16313409913q14.2 chr13: 46362859- 8 RB1 RB1 48209064 2q22.1 chr2: 138479322- 3LRP1B§ LRP1B 143365272 4q35.2 chr4: 186684565- 15 FRG2, 191273063 TUBB4Q5q11.2 chr5: 57754754- 5 PDE4D§ PLK2, 59053198 PDE4D 16p13.3 chr16:5062786- 2 A2BP1§ A2BP1 7709383 7q34 chr7: 141592807- 3 TRB@{circumflexover ( )} PRSS1 142264966 2q37.3 chr2: 241477619- 19 TMEM16G, 242951149ING5 19p13.3 chr19: 1-526082 10 GZMM, THEG, PPAP2C, C19orf20 10q23.31chr10: 89467202- 4 PTEN PTEN 90419015 8p23.2 chr8: 2053441- 1 CSMD1§CSMD1 6259545 1p36.31 chr1: 3756302- 23 DFFB, 6867390 ZBTB48, AJAP14q22.1 chr4: 91089383- 2 MGC48628 93486891 18q23 chr18: 75796373- 4PARD6G 76117153 6p25.3 chr6: 1543157- 2 FOXC1 2570302 19q13.43 chr19:63402921- 17 ZNF324 63811651 Xp21.2 chrX: 31041721- 2 DMD§ DMD 3456469711q25 chr11: 130280899- 12 OPCML§, HNT 134452384 HNT§ 13q12.11 chr13:1-23902184 29 LATS2 22q13.33 chr22: 45488286- 38 TUBGCP6 4969143215q11.2 chr15: 1-24740084 20 A26B1 22q11.22 chr22: 20517661- 3 VPREB121169423 10q26.3 chr10: 129812260- 35 MGMT, 135374737 SYCE1 12p13.2chr12: 11410696- 2 ETV6$ ETV6 12118386 8p23.3 chr8: 1-392555 2 ZNF5961p36.11 chr1: 26377344- 24 SFN 27532551 11p15.5 chr11: 1-1391954 49RASSF7 17q11.2 chr17: 26185485- 10 NF1 NF1 27216066 11q23.1 chr11:107086196- 61 ATM CADM1 116175885 9p24.3 chr9: 1-708871 5 FOXD4 10q11.23chr10: 52313829- 4 PRKG1§ DKK1, 53768264 PRKG1 15q15.1 chr15: 35140533-109 TUBGCP4 43473382 1p13.2 chr1: 110339388- 81 MAGI3 119426489 Xp22.33chrX: 1-3243111 21 SHOX 3p26.3 chr3: 1-2121282 2 CHL1 9p13.2 chr9:36365710- 2 PAX5 MELK 37139941 17p13.1 chr17: 7471230- 10 TP53 ATP1B27717938 12q24.33 chr12: 131913408- 7 CHFR 132349534 7q36.3 chr7:156893473- 7 PTPRN2§ NCAPG2 158821424 6q16.1 chr6: 76630464- 76 FUT9,105342994 C6orf165, C6orf162, GJA10 5q21.1 chr5: 85837489- 142 APC APC133480433 8p11.22 chr8: 39008109- 7 C8orf4, 41238710 ZMAT4 19q13.32chr19: 52031294- 25 BBC3 53331283 10p15.3 chr10: 1-1042949 4 TUBB81p31.1 chr1: 71284749- 4 NEGR1§ NEGR1 74440273 13q31.3 chr13: 92308911-2 GPC6§ GPC6, 94031607 DCT 16q11.2 chr16: 31854743- 37 RBL2 5352573920p13 chr20: 1-325978 10 SOX12 5q35.3 chr5: 177541057- 43 SCGB3A1180857866 1q43 chr1: 223876038- 173 RYR2§ FH, 247249719 ZNF678 16p13.3chr16: 1-359092 16 HBZ 17q21.2 chr17: 37319013- 22 CNP 37988602 2p25.3chr2: 1-15244284 51 MYT1L 3q13.31 chr3: 116900556- 1 LSAMP 1201073207q21.11 chr7: 65877239- 73 MAGI2§ CLDN4 79629882 7q35 chr7: 144118814- 3CNTNAP2§ CNTNAP2 148066271 14q32.12 chr14: 80741860- 154 PRIMA1106368585 16q24.3 chr16: 88436931- 9 C16orf3 88827254 3q26.31 chr3:175446835- 1 NAALADL2§ NAALADL2 178263192 17q25.3 chr17: 78087533- 8ZNF750 78774742 19p12 chr19: 21788507- 12 ZNF492, 34401877 ZNF99 12q23.1chr12: 97551177- 3 ANKS1B§ ANKS1B 99047626 4p16.3 chr4: 1-435793 4ZNF141 18p11.32 chr18: 1-587750 4 COLEC12 2q33.2 chr2: 204533830- 1PARD3B§ PARD3B 206266883 8p21.2 chr8: 22125332- 63 DPYSL2, 30139123STMN4 8q11.22 chr8: 42971602- 86 SNTG1§ FLJ23356, 72924037 ST18, RB1CC116q23.3 chr16: 80759878- 2 CDH13§ CDH13 82408573 11q14.1 chr11:82612034- 6 DLG2§ CCDC89, 85091467 CCDC90B, TMEM126A 14q23.3 chr14:65275722- 7 GPHN, 67085224 MPP5 7p22.2 chr7: 3046420- 1 SDK1§ SDK14279470 13q34 chr13: 111767404- 25 TUBGCP3 114142980 17p12 chr17:10675416- 5 MAP2K4 MAP2K4, 12635879 ZNF18 21q22.2 chr21: 38584860- 19DSCAM§, DSCAM 42033506 TMPRSS2/ERG$ 18q21.2 chr18: 46172638- 7 SMAD4,DCC 49935241 DCC§ 6q22.1 chr6: 101000242- 87 GTF3C6, 121511318 TUBE1,ROS1 14q11.2 chr14: 1-29140968 140 ZNF219, NDRG2

In various embodiments, it is contemplated to use the methods identifiedherein to identify CNVs of segments comprising the amplified regions orgenes identified in Table 5 and/or to use the methods identified hereinto identify CNVs of segments comprising the deleted regions or genesidentified in 6.

In one embodiment, the methods described herein provide a means toassess the association between gene amplification and the extent oftumor evolution. Correlation between amplification and/or deletion andstage or grade of a cancer may be prognostically important because suchinformation may contribute to the definition of a genetically basedtumor grade that would better predict the future course of disease withmore advanced tumors having the worst prognosis. In addition,information about early amplification and/or deletion events may beuseful in associating those events as predictors of subsequent diseaseprogression.

Gene amplification and deletions as identified by the method can beassociated with other known parameters such as tumor grade, histology,Brd/Urd labeling index, hormonal status, nodal involvement, tumor size,survival duration and other tumor properties available fromepidemiological and biostatistical studies. For example, tumor DNA to betested by the method could include atypical hyperplasia, ductalcarcinoma in situ, stage I-III cancer and metastatic lymph nodes inorder to permit the identification of associations betweenamplifications and deletions and stage. The associations made may makepossible effective therapeutic intervention. For example, consistentlyamplified regions may contain an overexpressed gene, the product ofwhich may be able to be attacked therapeutically (for example, thegrowth factor receptor tyrosine kinase, p185^(HER2)).

In various embodiments, the methods described herein can be used toidentify amplification and/or deletion events that are associated withdrug resistance by determining the copy number variation of nucleic acidsequences from primary cancers to those of cells that have metastasizedto other sites. If gene amplification and/or deletion is a manifestationof karyotypic instability that allows rapid development of drugresistance, more amplification and/or deletion in primary tumors fromchemoresistant patients than in tumors in chemosensitive patients wouldbe expected. For example, if amplification of specific genes isresponsible for the development of drug resistance, regions surroundingthose genes would be expected to be amplified consistently in tumorcells from pleural effusions of chemoresistant patients but not in theprimary tumors. Discovery of associations between gene amplificationand/or deletion and the development of drug resistance may allow theidentification of patients that will or will not benefit from adjuvanttherapy.

In a manner similar to that described for determining the presence orabsence of complete and/or partial fetal chromosomal aneuploidies in amaternal sample, methods, apparatus, and systems described herein can beused to determine the presence or absence of complete and/or partialchromosomal aneuploidies in any patient sample comprising nucleic acidse.g. DNA or cfDNA (including patient samples that are not maternalsamples). The patient sample can be any biological sample type asdescribed elsewhere herein. Preferably, the sample is obtained bynon-invasive procedures. For example, the sample can be a blood sample,or the serum and plasma fractions thereof. Alternatively, the sample canbe a urine sample or a fecal sample. In yet other embodiments, thesample is a tissue biopsy sample. In all cases, the sample comprisesnucleic acids e.g. cfDNA or genomic DNA, which is purified, andsequenced using any of the NGS sequencing methods described previously.

Both complete and partial chromosomal aneuploidies associated with theformation, and progression of cancer can be determined according to thepresent method.

In various embodiments, when using the methods described herein todetermine the presence and/or increased risk of cancer normalization ofthe data can be made with respect to the chromosome(s) for which the CNVis determined. In certain embodiments normalization of the data can bemade with respect to the chromosome arm(s) for which the CNV isdetermined. In certain embodiments, normalization of the data can bemade with respect to the particular segment(s) for which the CNV isdetermined.

In addition to the role of CNV in cancer, CNVs have been associated witha growing number of common complex disease, including humanimmunodeficiency virus (HIV), autoimmune diseases and a spectrum ofneuropsychiatric disorders.

CNVs in Infectious and Autoimmune Disease

To date a number of studies have reported association between CNV ingenes involved in inflammation and the immune response and HIV, asthma,Crohn's disease and other autoimmune disorders (Fanciulli et al., ClinGenet 77:201-213 [2010]). For example, CNV in CCL3L1, has beenimplicated in HIV/AIDS susceptibility (CCL3L1, 17q11.2 deletion),rheumatoid arthritis (CCL3L1, 17q11.2 deletion), and Kawasaki disease(CCL3L1, 17q11.2 duplication); CNV in HBD-2, has been reported topredispose to colonic Crohn's disease (HDB-2, 8p23.1 deletion) andpsoriasis (HDB-2, 8p23.1 deletion); CNV in FCGR3B, was shown topredispose to glomerulonephritis in systemic lupus erthematosous(FCGR3B, 1q23 deletion, 1q23 duplication), anti-neutrophil cytoplasmicantibody (ANCA)-associated vasculatis (FCGR3B, 1q23 deletion), andincrease the risk of developing rheumatoid arthritis. There are at leasttwo inflammatory or autoimmune diseases that have been shown to beassociated with CNV at different gene loci. For example, Crohn's diseaseis associated with low copy number at HDB-2, but also with a commondeletion polymorphism upstream of the IGRM gene that encodes a member ofthe p47 immunity-related GTPase family. In addition to the associationwith FCGR3B copy number, SLE susceptibility has also been reported to besignificantly increased among subjects with a lower number of copies ofcomplement component C4.

Associations between genomic deletions at the GSTM1 (GSTM1,1q23deletion) and GSTT1 (GSTT1, 22q11.2 deletion) loci and increasedrisk of atopic asthma have been reported in a number of independentstudies. In some embodiments, the methods described herein can be usedto determine the presence or absence of a CNV associated withinflammation and/or autoimmune diseases. For example, the methods can beused to determine the presence of a CNV in a patient suspected to besuffering from HIV, asthma, or Crohn's disease. Examples of CNVassociated with such diseases include without limitation deletions at17q11.2, 8p23.1, 1q23, and 22q11.2, and duplications at 17q11.2, and1q23. In some embodiments, the present method can be used to determinethe presence of CNV in genes including but not limited to CCL3L1, HBD-2,FCGR3B, GSTM, GSTT1, C4, and IRGM.

CNV Diseases of the Nervous System

Associations between de novo and inherited CNV and several commonneurological and psychiatric diseases have been reported in autism,schizophrenia and epilepsy, and some cases of neurodegenerative diseasessuch as Parkinson's disease, amyotrophic lateral sclerosis (ALS) andautosomal dominant Alzheimer's disease (Fanciulli et al., Clin Genet77:201-213 [2010]). Cytogenetic abnormalities have been observed inpatients with autism and autism spectrum disorders (ASDs) withduplications at 15q11-q13. According to the Autism Genome projectConsortium, 154 CNV including several recurrent CNVs, either onchromosome 15q11-q13 or at new genomic locations including chromosome2p16, 1q21 and at 17p12 in a region associated with Smith-Magenissyndrome that overlaps with ASD. Recurrent microdeletions ormicroduplications on chromosome 16p11.2 have highlighted the observationthat de novo CNVs are detected at loci for genes such as SHANK3 (22q13.3deletion), neurexin 1 (NRXN1, 2p16.3 deletion) and the neuroglins(NLGN4, Xp22.33 deletion) that are known to regulate synapticdifferentiation and regulate glutaminergic neurotransmitter release.Schizophrenia has also been associated with multiple de novo CNVs.Microdeletions and microduplications associated with schizophreniacontain an overrepresentation of genes belonging to neurodevelopmentaland glutaminergic pathways, suggesting that multiple CNVs affectingthese genes may contribute directly to the pathogenesis of schizophreniae.g. ERBB4, 2q34 deletion, SLC1A3, 5p13.3 deletion; RAPEGF4, 2q31.1deletion; CIT, 12.24 deletion; and multiple genes with de novo CNV. CNVshave also been associated with other neurological disorders includingepilepsy (CHRNA7, 15q13.3 deletion), Parkinson's disease (SNCA 4q22duplication) and ALS (SMN1, 5q12.2.-q13.3 deletion; and SMN2 deletion).In some embodiments, the methods described herein can be used todetermine the presence or absence of a CNV associated with diseases ofthe nervous system. For example, the methods can be used to determinethe presence of a CNV in a patient suspected to be suffering fromautisim, schizophrenia, epilepsy, neurodegenerative diseases such asParkinson's disease, amyotrophic lateral sclerosis (ALS) or autosomaldominant Alzheimer's disease. The methods can be used to determine CNVof genes associated with diseases of the nervous system includingwithout limitation any of the Autism Spectrum Disorders (ASD),schizophrenia, and epilepsy, and CNV of genes associated withneurodegenerative disorders such as Parkinson's disease. Examples of CNVassociated with such diseases include without limitation duplications at15q11-q13, 2p16, 1q21, 17p12, 16p11.2, and 4q22, and deletions at22q13.3, 2p16.3, Xp22.33, 2q34, 5p13.3, 2q31.1, 12.24, 15q13.3, and5q12.2. In some embodiments, the methods can be used to determine thepresence of CNV in genes including but not limited to SHANK3, NLGN4,NRXN1, ERBB4, SLC1A3, RAPGEF4, CIT, CHRNA7, SNCA, SMN1, and SMN2.

CNV and Metabolic or Cardiovascular Diseases

The association between metabolic and cardiovascular traits, such asfamilial hypercholesterolemia (FH), atherosclerosis and coronary arterydisease, and CNVs has been reported in a number of studies (Fanciulli etal., Clin Genet 77:201-213 [2010]). For example, germlinerearrangements, mainly deletions, have been observed at the LDLR gene(LDLR, 19p13.2 deletion/duplication) in some FH patients who carry noother LDLR mutations. Another example is the LPA gene that encodesapolipoprotein(a) (apo(a)) whose plasma concentration is associated withrisk of coronary artery disease, myocardial infarction (MI) and stroke.Plasma concentrations of the apo(a) containing lipoprotein Lp(a) varyover 1000-fold between individuals and 90% of this variability isgenetically determined at the LPA locus, with plasma concentration andLp(a) isoform size being proportional to a highly variable number of‘kringle 4’ repeat sequences (range 5-50). These data indicate that CNVin at least two genes can be associated with cardiovascular risk. Themethods described herein can be used in large studies to searchspecifically for CNV associations with cardiovascular disorders. In someembodiments, the present method can be used to determine the presence orabsence of a CNV associated with metabolic or cardiovascular disease.For example, the present method can be used to determine the presence ofa CNV in a patient suspected to be suffering from familialhypercholesterolemia. The methods described herein can be used todetermine CNV of genes associated with metabolic or cardiovasculardisease e.g. hypercholesterolemia. Examples of CNV associated with suchdiseases include without limitation 19p13.2 deletion/duplication of theLDLR gene, and multiplications in the LPA gene.

Determination of Complete Chromosomal Aneuploidies in Patient Samples

In one embodiment, method are provided for determining the presence orabsence of any one or more different complete chromosomal aneuploidiesin a patient test sample comprising nucleic acid molecules. In someembodiments, the method determines the presence or absence of any one ormore different complete chromosomal aneuploidies. The steps of themethod comprise (a) obtaining sequence information for the patientnucleic acids in the patient test sample; and (b) using the sequenceinformation to identify a number of sequence tags for each of any one ormore chromosomes of interest selected from chromosomes 1-22, X and Y andto identify a number of sequence tags for a normalizing chromosomesequence for each of the any one or more chromosomes of interest. Thenormalizing chromosome sequence can be a single chromosome, or it can bea group of chromosomes selected from chromosomes 1-22, X, and Y. Themethod further uses in step (c) the number of sequence tags identifiedfor each of the any one or more chromosomes of interest and the numberof sequence tags identified for each normalizing chromosome sequence tocalculate a single chromosome dose for each of the any one or morechromosomes of interest; and (d) compares each of the single chromosomedoses for each of the any one or more chromosomes of interest to athreshold value for each of the one or more chromosomes of interest,thereby determining the presence or absence of any one or more differentcomplete patient chromosomal aneuploidies in the patient test sample.

In some embodiments, step (c) comprises calculating a single chromosomedose for each chromosomes of interest as the ratio of the number ofsequence tags identified for each of the chromosomes of interest and thenumber of sequence tags identified for the normalizing chromosome foreach of the chromosomes of interest.

In other embodiments, step (c) comprises calculating a single chromosomedose for each of the chromosomes of interest as the ratio of the numberof sequence tags identified for each of the chromosomes of interest andthe number of sequence tags identified for the normalizing chromosomefor each of the chromosomes of interest. In other embodiments, step (c)comprises calculating a sequence tag ratio for a chromosome of interestby relating the number of sequence tags obtained for the chromosome ofinterest to the length of the chromosome of interest, and relating thenumber of tags for the corresponding normalizing chromosome sequence forthe chromosome of interest to the length of the normalizing chromosomesequence, and calculating a chromosome dose for the chromosome ofinterest as a ratio of the sequence tags density of the chromosome ofinterest and the sequence tag density for the normalizing sequence. Thecalculation is repeated for each of all chromosomes of interest. Steps(a)-(d) can be repeated for test samples from different patients.

An example of the embodiment whereby one or more complete chromosomalaneuploidies are determined in a cancer patient test sample comprisingcell-free DNA molecules, comprises: (a) sequencing at least a portion ofcell-free DNA molecules to obtain sequence information for the patientcell-free DNA molecules in the test sample; (b) using the sequenceinformation to identify a number of sequence tags for each of any twentyor more chromosomes of interest selected from chromosomes 1-22, X, and Yand to identify a number of sequence tags for a normalizing chromosomefor each of the twenty or more chromosomes of interest; (c) using thenumber of sequence tags identified for each of the twenty or morechromosomes of interest and the number of sequence tags identified foreach the normalizing chromosome to calculate a single chromosome dosefor each of the twenty or more chromosomes of interest; and (d)comparing each of the single chromosome doses for each of the twenty ormore chromosomes of interest to a threshold value for each of the twentyor more chromosomes of interest, and thereby determining the presence orabsence of any twenty or more different complete chromosomalaneuploidies in the patient test sample.

In another embodiment, the method for determining the presence orabsence of any one or more different complete chromosomal aneuploidiesin a patient test sample as described above uses a normalizing segmentsequence for determining the dose of the chromosome of interest. In thisinstance, the method comprises (a) obtaining sequence information forthe nucleic acids in the sample; (b) using the sequence information toidentify a number of sequence tags for each of any one or morechromosomes of interest selected from chromosomes 1-22, X and Y and toidentify a number of sequence tags for a normalizing segment sequencefor each of any one or more chromosomes of interest. The normalizingsegment sequence can be a single segment of a chromosome or it can be agroup of segments form one or more different chromosomes. The methodfurther uses in step (c) the number of sequence tags identified for eachof said any one or more chromosomes of interest and said number ofsequence tags identified for said normalizing segment sequence tocalculate a single chromosome dose for each of said any one or morechromosomes of interest; and (d) comparing each of said singlechromosome doses for each of said any one or more chromosomes ofinterest to a threshold value for each of said one or more chromosomesof interest, and thereby determining the presence or absence of one ormore different complete chromosomal aneuploidies in the patient sample.

In some embodiments, step (c) comprises calculating a single chromosomedose for each of said chromosomes of interest as the ratio of the numberof sequence tags identified for each of said chromosomes of interest andthe number of sequence tags identified for said normalizing segmentsequence for each of said chromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tagratio for a chromosome of interest by relating the number of sequencetags obtained for the chromosome of interest to the length of thechromosome of interest, and relating the number of tags for thecorresponding normalizing segment sequence for the chromosome ofinterest to the length of the normalizing segment sequence, andcalculating a chromosome dose for the chromosome of interest as a ratioof the sequence tags density of the chromosome of interest and thesequence tag density for the normalizing segment sequence. Thecalculation is repeated for each of all chromosomes of interest. Steps(a)-(d) can be repeated for test samples from different patients.

A means for comparing chromosome doses of different sample sets isprovided by determining a normalized chromosome value (NCV), whichrelates the chromosome dose in a test sample to the mean of the of thecorresponding chromosome dose in a set of qualified samples. The NCV iscalculated as:

${NCV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thchromosome dose in a set of qualified samples, and x_(ij) is theobserved j-th chromosome dose for test sample i.

In some embodiments, the presence or absence of one complete chromosomalaneuploidy is determined. In other embodiments, the presence or absenceof two, three, four, five, six, seven, eight, nine, ten, eleven, twelve,thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen,twenty, twenty-one, twenty-two, twenty-three, or twenty four completechromosomal aneuploidies are determined in a sample, wherein twenty-twoof the complete chromosomal aneuploidies correspond to completechromosomal aneuploidies of any one or more of the autosomes; thetwenty-third and twenty fourth chromosomal aneuploidy correspond to acomplete chromosomal aneuploidy of chromosomes X and Y. As aneuploidiescan comprise trisomies, tetrasomies, pentasomies and other polysomies,and the number of complete chromosomal aneuploidies varies in differentdiseases and in different stages of the same disease, the number ofcomplete chromosomal aneuploidies that are determined according to thepresent method are at least 24, at least 25, at least 26, at least 27,at least 28, at least 29, at least 30 complete, at least 40, at least50, at least 60, at least 70, at least 80, at least 90, at least 100 ormore chromosomal aneuploidies. Systematic karyotyping of tumors hasrevealed that the chromosome number in cancer cells is highly variable,ranging from hypodiploidy (considerably fewer than 46 chromosomes) totetraploidy and hypertetraploidy (up to 200 chromosomes) (Storchova andKuffer J Cell Sci 121:3859-3866 [2008]). In some embodiments, the methodcomprises determining the presence or absence of up to 200 or morechromosomal aneuploidies in a sample form a patient suspected or knownto be suffering from cancer e.g. colon cancer. The chromosomalaneuploidies include losses of one or more complete chromosomes(hypodiploidies), gains of complete chromosomes including trisomies,tetrasomies, pentasomies, and other polysomies. Gains and/or losses ofsegments of chromosomes can also be determined as described elsewhereherein. The method is applicable to determining the presence or absenceof different aneuploidies in samples from patients suspected or known tobe suffering from any cancer as described elsewhere herein.

In some embodiments, any one of chromosomes 1-22, X and Y, can be thechromosome of interest in determining the presence or absence of any oneor more different complete chromosomal aneuploidies in a patient testsample as described above. In other embodiments, two or more chromosomesof interest are selected from any two or more of chromosomes 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, orY. In one embodiment, any one or more chromosomes of interest areselected from chromosomes 1-22, X, and Y comprise at least twentychromosomes selected from chromosomes 1-22, X, and Y, and wherein thepresence or absence of at least twenty different complete chromosomalaneuploidies is determined. In other embodiments, any one or morechromosomes of interest selected from chromosomes 1-22, X, and Y is allof chromosomes 1-22, X, and Y, and wherein the presence or absence ofcomplete chromosomal aneuploidies of all of chromosomes 1-22, X, and Yis determined. Complete different chromosomal aneuploidies that can bedetermined include complete chromosomal monosomies of any one or more ofchromosomes 1-22, X and Y; complete chromosomal trisomies of any one ormore of chromosomes 1-22, X and Y; complete chromosomal tetrasomies ofany one or more of chromosomes 1-22, X and Y; complete chromosomalpentasomies of any one or more of chromosomes 1-22, X and Y; and othercomplete chromosomal polysomies of any one or more of chromosomes 1-22,X and Y.

Determination of Partial Chromosomal Aneuploidies in Patient Samples

In another embodiment, methods for determining the presence or absenceof any one or more different partial chromosomal aneuploidies in apatient test sample comprising nucleic acid molecules are provided. Thesteps of the method comprise (a) obtaining sequence information for thepatient nucleic acids in the sample; and (b) using the sequenceinformation to identify a number of sequence tags for each of any one ormore segments of any one or more chromosomes of interest selected fromchromosomes 1-22, X, and Y and to identify a number of sequence tags fora normalizing segment sequence for each of any one or more segments ofany one or more chromosomes of interest. The normalizing segmentsequence can be a single segment of a chromosome or it can be a group ofsegments form one or more different chromosomes. The method further usesin step (c) the number of sequence tags identified for each of any oneor more segments of any one or more chromosomes of interest and thenumber of sequence tags identified for the normalizing segment sequenceto calculate a single segment dose for each of any one or more segmentsof any one or more chromosome of interest; and (d) comparing each of thesingle chromosome doses for each of any one or more segments of any oneor more chromosomes of interest to a threshold value for each of saidany one or more chromosomal segments of any one or more chromosome ofinterest, and thereby determining the presence or absence of one or moredifferent partial chromosomal aneuploidies in said sample.

In some embodiments, step (c) comprises calculating a single segmentdose for each of any one or more segments of any one or more chromosomesof interest as the ratio of the number of sequence tags identified foreach of any one or more segments of any one or more chromosomes ofinterest and the number of sequence tags identified for the normalizingsegment sequence for each of any one or more segments of any one or morechromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tagratio for a segment of interest by relating the number of sequence tagsobtained for the segment of interest to the length of the segment ofinterest, and relating the number of tags for the correspondingnormalizing segment sequence for the segment of interest to the lengthof the normalizing segment sequence, and calculating a segment dose forthe segment of interest as a ratio of the sequence tags density of thesegment of interest and the sequence tag density for the normalizingsegment sequence. The calculation is repeated for each of allchromosomes of interest. Steps (a)-(d) can be repeated for test samplesfrom different patients.

A means for comparing segment doses of different sample sets is providedby determining a normalized segment value (NSV), which relates thesegment dose in a test sample to the mean of the of the correspondingsegment dose in a set of qualified samples. The NSV is calculated as:

${NSV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thsegment dose in a set of qualified samples, and x_(ij) is the observedj-th segment dose for test sample i.

In some embodiments, the presence or absence of one partial chromosomalaneuploidy is determined. In other embodiments, the presence or absenceof two, three, four, five, six, seven, eight, nine, ten, fifteen,twenty, twenty-five, or more partial chromosomal aneuploidies aredetermined in a sample. In one embodiment, one segment of interestselected from any one of chromosomes 1-22, X, and Y is selected fromchromosomes 1-22, X, and Y. In another embodiment, two or more segmentsof interest selected from chromosomes 1-22, X, and Y are selected fromany two or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, anyone or more segments of interest are selected from chromosomes 1-22, X,and Y comprise at least one, five, ten, 15, 20, 25, 50, 75, 100 or moresegments selected from chromosomes 1-22, X, and Y, and wherein thepresence or absence of at least one, five, ten, 15, 20, 25, 50, 75, 100,or more different partial chromosomal aneuploidies is determined.Different partial chromosomal aneuploidies that can be determinedinclude chromosomal aneuploidies include partial duplications, partialmultiplications, partial insertions and partial deletions.

Samples that can be used for determining the presence or absence of achromosomal aneuploidy (partial or complete) in a patient can be any ofthe biological samples described elsewhere herein. The type of sample orsamples that can be used for the determination of aneuploidy in apatient will depend on the type of disease from which the patient isknown or suspected to be suffering. For example, a stool sample can bechosen as a source of DNA to determine the presence or absence ofaneuploidies associated with colorectal cancer. The method is alsoapplicable to tissue samples as described herein. Preferably, the sampleis a biological sample that is obtained by non-invasive means e.g. aplasma sample. As described elsewhere herein, sequencing of the nucleicacids in the patient sample can be performed using next generationsequencing (NGS) as described elsewhere herein. In some embodiments,sequencing is massively parallel sequencing usingsequencing-by-synthesis with reversible dye terminators. In otherembodiments, sequencing is sequencing-by-ligation. In yet otherembodiments, sequencing is single molecule sequencing. Optionally, anamplification step is performed prior to sequencing.

In some embodiments, the presence or absence of an aneuploidy isdetermined in a patient suspected to be suffering from a cancer asdescribed elsewhere herein e.g. lung, breast, kidney, head and neck,ovary, cervix, colon, pancreas, esophagus, bladder and other organs, andblood cancers. Blood cancers include cancers of the bone marrow, blood,and lymphatic system, which includes lymph nodes, lymphatic vessels,tonsils, thymus, spleen, and digestive tract lymphoid tissue. Leukemiaand myeloma, which start in the bone marrow, and lymphoma, which startsin the lymphatic system, are the most common types of blood cancer.

The determination of the presence or absence of one or more chromosomalaneuploidies in a patient sample can be made without limitation todetermine the predisposition of the patient to a particular cancer, todetermine the presence or absence of a cancer as part of routine screenin patients known and not known to be predisposed to the cancer inquestion, to provide a prognosis for the disease, to assess the need foradjuvant therapy, and to determine the progress or regress of thediseases.

Genetic Counseling

Fetal chromosome abnormalities are a major contributor to miscarriages,congenital anomalies, and perinatal deaths (Wellesley et al. Europ. J.Human Genet., 20: 521-526 [2012]; Nagaoka et al. Nature Rev. Genetics13: 493-504 [2012]). Since the introduction of amniocentesis, followedby the introduction of chorionic villus sampling (CVS), pregnant womenhave had options to obtain information about fetal chromosome status(ACOG Practice Bulletin No. 77: Obstet Gynecol 109: 217-227 [2007]).Cytogenetic karyotyping of fetal cells or chorionic villi obtained fromthese procedures leads to diagnosis in the vast majority of cases withvery high sensitivity and specificity (˜99%) when adequate tissue isobtained (Hahnemann and Vejerslev, Prenat Diagn., 17: 801-820 1997;NICHD National Registry for Amniocentesis Study JAMA 236:1471-1476[1976]). However, these procedures also pose risks to the fetus andpregnant woman (Odibo et al. Obstet Gynecol 112: 813-819 [2008]; Odiboet al. Obstet Gynecol 111: 589-595 [2008]).

To mitigate these risks, a series of prenatal screening algorithms havebeen developed to stratify women for their likelihood of the most commonfetal trisomies-T21 (Down syndrome) and trisomy 18 (T18, Edwardssyndrome) and to a lesser extent trisomy 13 (T13, Patau syndrome). Thescreens typically involve measurement of multiple biochemical analytesin the maternal serum at different time points combined withultrasonographic measurement of the fetal nuchal translucency (NT) andincorporation of other maternal factors, such as age to generate a riskscore. Based on their development and refinement over the years anddepending on when the screening is administered (first or secondtrimester only, sequential, or fully integrated) and how the screeningis administered (serum-only or serum combined with NT), a menu ofoptions has evolved with variable detection rates (65 to 90%) and highscreen positive rates (5%) (ACOG Practice Bulletin No. 77: ObstetGynecol 109: 217-227 [2007]).

For patients, following this multi-step process, the resultantinformation or “risk score” can be confusing and anxiety provoking,particularly in the absence of comprehensive counseling. Ultimately, theresults are weighed against the risks for miscarriage from an invasiveprocedure in a woman's decision-making. Better noninvasive means toobtain more definitive information on fetal chromosomal statusfacilitates decision making in this context.

Such improved noninvasive means of obtaining more definitive informationon fetal chromosomal status are believed to be provided by methodsdescribed herein.

In various embodiments, genetic counseling is contemplated as acomponent of the use of the assays described herein, particularly in aclinical context. Conversely, the aneuploidy detection methods describedherein can comprise one option offered in the context of prenatal careand associated genetic counseling.

Accordingly, in various embodiments the methods described herein canoffered as a primary screen (e.g., for women with an a priori pregnancyrisk) or as a secondary screen to those women with a positive“conventional” screen. In certain embodiments, it is contemplated thatthe non-invasive prenatal testing (NIPT) methods described hereinadditionally comprise a genetic counseling component and/or that geneticcounseling and pregnancy “management”, optionally, or definitivelyincorporated the NIPT methods described herein.

For example, in certain embodiments, women present with one or more apriori pregnancy risks. Such risks include, but are not limited to oneor more of the following:

1) Maternal age over 35 although it is noted that approximately 80% ofchildren born with Down's syndrome are born to women under the age of35.

2) Previous fetus/child with autosomal trisomy. It is believed that therecurrence rate is about 1.6 to about 8.2 times the maternal age riskdepending on the type of trisomy, whether the previous pregnancy wasspontaneously aborted, and the maternal age at the initial occurrenceand the mother's age at subsequent prenatal diagnosis.

3) Previous fetus/child with sex chromosome abnormality—not all sexabnormalities have a maternal origin and not all have risk ofrecurrence. When they do, the recurrence rate is about 1.6 to about 1.5times the maternal age risk.

4) Parental carrier of chromosomal translocation.

5) Parental carrier of chromosome inversion.

6) Parental aneuploidy or mosaicism.

7) Use of certain assisted reproductive technologies.

In such circumstances, the mother, e.g., in consultation with aphysician, genetic counselor, and the like, may be offered use of themethods described herein for non-invasive determination of the presenceor absence of a fetal aneuploidy (e.g., trisomy 21, trisomy 18, trisomy13, monosomy X etc.) subject to the various considerations describedbelow. In this regard, it is noted that the methods described herein arebelieved to be effective even in the first trimesters. Thus, in certainembodiments, use of the NIPT methods described herein is contemplated asearly as 8 weeks, and in various embodiments at about 10 weeks or later.

In certain embodiments, the methods described herein can be offered as asecondary screen to those women with a positive “conventional” screen.For example, in certain embodiments, pregnant women may present with astructural abnormality such as fetal cystic hygroma, or increased nuchaltranslucency, e.g., as detected using ultrasonography. Typicallyultrasound for structural defects is performed in weeks 18-22 and,particularly when an irregularity is observed, may be coupled with afetal echocardiogram. It is contemplated that when an abnormality isobserved (e.g., a positive “conventional” screen), the mother, e.g., inconsultation with a physician, genetic counselor, and the like, may beoffered use of the methods described herein for non-invasivedetermination of the presence or absence of a fetal aneuploidy (e.g.,trisomy 21, trisomy 18, trisomy 13, monosomy X etc.) subject to thevarious considerations described below.

Thus, in various embodiments, genetic counseling is contemplated inwhich the (NIPT) assays described herein are offered as a component ofprenatal care, the management of pregnancy and/or the development/designof a birth plan. By offering NIPT as a secondary screen to those womenwith a positive conventional screen (or other a priori risk), the numberof unnecessary amniocentesis and CVS procedures are expected todecrease. However, the need for genetic counseling will increase, asinformed consent is an important component of NIPT.

Since a positive NIPT result (using the methods described herein) ismore similar to a positive result from amniocentesis or CVS, in geneticcounseling women should be given the opportunity prior to this testingto decide whether they desire this degree of information. Pre-testgenetic counseling for NIPT should also include discussion/lrecommendation for confirmation of abnormal test results via CVS,amniocentesis, cordocentesis, etc (depending upon gestational age), sothat appropriate consideration can be given to the expected timing ofresults for post-test planning. Per the National Society of GeneticCounselors (NSGC, USA) statements on the topic (see, e.g., Devers et al.Noninvasive Prenatal Testing/Noninvasive Prenatal Diagnosis: theposition of the National Society of Genetic Counselors (by NSGC PublicPolicy Committee). NSGC Position Statements 2012; Benn et al., PrenatDiagn, 31: 519-522 [2011]), because NIPT does not presently screen forall chromosomal or genetic conditions, it may not replace standard riskassessment and prenatal diagnosis. It is contemplated that patients withother factors (e.g., certain abnormal ultrasound findings) suggestive ofchromosome abnormality should receive genetic counseling in which theyare provided the option of conventional confirmatory diagnostic testing.regardless of NIPT results. In genetic counseling women should also bemade aware that for some patients an NIPT result may not be informative.

NIPT using the methods described herein is perhaps more similar to CVSthan amniocentesis in that detection of aneuploidy is typicallyrepresentative of the chromosomal constitution of the fetus, but in someinstances may be representative of confined placental aneuploidy orconfined placental mosaicism (CPM). CPM occurs in approximately 1-2% ofcases of CVS results today, and some women undergo an amniocentesis atlater gestational age after CVS to make the distinction betweenapparently isolated placental aneuploidy versus fetal aneuploidy. AsNIPT is implemented more widely, cases of CPM are expected to cause somenumber of positive NIPT results that may not be subsequently confirmedby invasive procedure, particularly amniocentesis. Again, in variousembodiments, it is contemplated that this information is presented tothe patient in the context of genetic counseling (e.g., by physician,genetic counselor, etc.).

It will be recognized that in various embodiments, a component ofgenetic counseling may be to recommend confirmatory diagnostics, toinform regarding risk levels and timing for various confirmatorydiagnostics can to provide input as to the value of the informationprovided by such confirmatory methods, particularly in the context ofthe timing of the pregnancy. In various embodiments the geneticcounseling can also establish a plan to monitor the pregnancy (e.g.,follow-up ultrasound, additional physician visits, and the like) and toset up a series of decision points where appropriate. In addition, thegenetic counseling can suggest and aid in development of a birth planthat can include for example, decisions regarding the site of delivery(e.g., home, hospital, specialized facility, etc.), the staff involvedat the site of delivery, available tertiary care for the infant, and thelike.

While the foregoing discussion focuses on the methods described hereinas a component (and perhaps secondary tool) in prenatal diagnosis, asclinical experience accumulates and if results are successful fromcomparative studies to conventional screening, it is possible that theNIPT methods described herein can replace current screening protocolsand possibly serve as a primary tool.

It is also contemplated that the methods described herein will find useon pregnancies with multiple gestations.

Typically, it is expected that genetic counseling, e.g., as describedabove, may be provided by a physician (e.g., primary physician,obstetrician, etc.) and/or by a genetic counselor, or other qualifiedmedical professional. In certain embodiments the counseling is providedface-to-face, however, it is recognized that in certain instances, thecounseling can be provided through remote access (e.g., via text, cellphone, cell phone app, tablet app, internet, and the like).

It is also recognized, that in certain embodiments, the geneticcounseling or a component thereof can be delivered by a computer system.For example, “smart advice” systems can be provided that in response totest results, instructions from a medical care provider, and/or inresponse to queries (e.g., from a patient) provide genetic counselinginformation (e.g., as described above). In certain embodiments theinformation will be specific to clinical information provided by thephysician, healthcare system, and/or patient. In certain embodiments theinformation can provided in an iterative manner. Thus, for example, thepatient can provide “what if” inquiries and the system can returninformation such as diagnostic options, risk factors, timing, andimplication of various outcomes.

In certain embodiments the information can be provided in a transitorymanner (e.g., presented on a computer screen). In certain embodiments,the information can be provided in a non-transitory manner. Thus, forexample, the information can be printed out (e.g., as a list of optionsand/or recommendations optionally with associated timing, etc.) and/orstored on computer readable media (e.g., magnetic media such as a localhard drive, a server, etc., optical media, flash memory, and the like).

It will be appreciated that typically such systems will be configured toprovide adequate security such that patient privacy is maintained, e.g.,according to prevailing standards in the industry.

The foregoing discussion of genetic counseling is intended to beillustrative and not limiting. Genetic counseling is a well-establishedbranch of medical science and incorporation of a counseling componentwith respect to the assays described herein is within the scope andskill of the practitioner. Moreover, it is recognized that as the fieldprogresses, the nature of genetic counseling and associated informationand recommendations is likely to alter.

Determination of Fetal Fraction

Methods of fetal fraction determination are disclosed in U.S. patentapplication Ser. No. 12/958,347 filed Dec. 1, 2010, U.S. patentapplication Ser. No. 13/365,240 filed Feb. 2, 2012, and U.S. patentapplication Ser. No. 13/445,778 filed Apr. 12, 2012, which areincorporated herein by reference in their entireties. A full discussionof the techniques for determining fetal fraction can be found in thesedocuments.

The methods described herein enable determination of fetal fraction in asample comprising a mixture of fetal and maternal nucleic acids, or moregenerally a mixture of nucleic acids having their origin in twodifferent genomes. For purposes of this discussion, maternal and fetalnucleic acids will be described, but it should be understood that anytwo genomes can be substituted therefore. In some embodiments, fetalfraction is determined concurrently with determining the presence orabsence of a copy number variation such as aneuploidy. As described morefully below, one set of tags of from a test sample may be employed todetermine both fetal fraction and copy number variation.

Methods for quantifying fetal fraction rely on differences between thefetal and the maternal genome. In certain embodiments described herein,determination of fetal fraction of sample DNA relies on multiple DNAsequence readings at sequence sites known to harbor one or morepolymorphisms. In some embodiments, the polymorphism sites or targetnucleic acid sequences are discovered while aligning sequence tags toone another and/or a reference sequence. In certain embodiments, thefetal fraction of sample DNA is determined by considering copy numberinformation for a particular chromosome or chromosome sequence wherethere is a copy number difference between the maternal chromosome andthe fetal chromosome. In such embodiments, the fetal fraction of sampleDNA is determined by considering the relative amounts of sample DNA fromthe mother and fetus that originated with a chromosome or segmentdetermined or known to have a copy number variation. In suchembodiments, fetal fraction may be calculated using copy numbervariations between maternal and fetal chromosomes. For this purpose, themethod and apparatus may calculate a normalized chromosome value (NCV)as described below, or a similar metric.

Some methods are limited by the gender of the fetus, e.g., methods forquantifying fetal fraction that rely on the presence of sequences thatare specific to the Y chromosome or determine the chromosome dose of Xchromosome for a male fetus. In some embodiments, quantification offetal DNA is directed toward fetal targets that have that either have nomaternal counterparts e.g. Y chromosome sequences (Fan et al., Proc NatlAcad Sci 105:16266-16271 [2008] and US Patent Application PublicationNo. 2010/0112590, filed Nov. 6, 2009, Lo et al.) or the RHD1 gene in anRhD-negative mother, or differ from the maternal background by atmultiple DNA base pairs. Other methods are independent of the gender ofthe fetus, and rely on polymorphic differences between the fetal andmaternal genomes.

Allelic imbalances in polymorphisms can be detected and quantified byvarious techniques. In some embodiments, digital PCR is used todetermine an allelic imbalance of polymorphisms e.g. a SNP on mRNA.Alternatively, capillary gel electrophoresis is used to detectdifferences in the size of the polymorphic region e.g. as in the case ofan STR.

In some embodiments, epigenetic differences can be detected e.g.differential methylation of promoter regions, can be used alone or incombination with digital PCR to determine differences between the fetaland maternal genomes and quantitify fetal fraction (Tong et al., ClinChem 56:90-98 [2010]). Modifications of epigenetic methods are alsoincluded e.g methylation-based DNA discrimination, (Erich et al., AJOG204: pages 205.e1-205.e11[2011]). In some embodiments, the fetalfraction is estimated using sequencing of preselected panel(s) ofpolymorphic sequences as described elsewhere herein.

Methods for quantifying fetal DNA in maternal plasma include withoutlimitation and in addition to the method of sequencing panels ofpreselected polymorphic sequences as described elsewhere herein,real-time qPCR, mass spectrometry, digital PCR including microfluidicdigital PCR, capillary gel electrophoresis.

The discussion in this section initially considers fetal fraction asdetermined from one or more polymorphisms or other information fromchromosomes or chromosome segments that do not (or are determined notto) have copy number variations. Fetal fraction determined by suchtechniques will be referred to herein as non-CNV fetal fraction or“NCNFF.” Later in this section, techniques are described for calculatingfetal fraction from chromosomes or chromosome segments determined topossess copy number variations. Fetal fraction determined from suchtechniques will be referred to herein as CNV fetal fraction or “CNFF.”

In some embodiments, the fetal fraction is evaluated by determining therelative contribution of a polymorphic allele derived from the fetalgenome and the contribution of the corresponding polymorphic allelederived from the maternal genome. In some embodiments, the fetalfraction is evaluated by determining the relative contribution of apolymorphic allele derived from the fetal genome to the totalcontribution of the corresponding polymorphic allele derived from boththe fetal and the maternal genome.

Polymorphisms can be indicative, informative, or both. Indicativepolymorphisms indicate the presence of fetal cell-free DNA (“cfDNA”) ina maternal sample. Informative polymorphisms, such as informative SNPs,yield information about the fetus—for example, the presence or absenceof a disease, genetic abnormality, or any other biological informationsuch as the stage of gestation or gender. Informative polymorphisms inthis instance are those which identify differences between the sequenceof the mother and the fetus and are used in the methods disclosedherein. Stated another way, informative polymorphisms are polymorphismsin a nucleic acid sample that possess different sequences (i.e., theypossess different alleles) and the sequences are present in differentamounts. The different amounts of the sequences/alleles are used in someof the methods herein to determine fetal fraction, particularly NCNFF.

Polymorphic sites include, without limitation, single nucleotidepolymorphisms (SNPs), tandem SNPs, small-scale multi-base deletions orinsertions (IN-DELS or deletion insertion polymorphisms (DIPs)),Multi-Nucleotide Polymorphisms (MNPs), Short Tandem Repeats (STRs),restriction fragment length polymorphisms (RFLP), or any polymorphismspossessing any other allelic variation of sequence in a chromosome. Insome embodiments, each target nucleic acid comprises two tandem SNPs.The tandem SNPs are analyzed as a single unit (e.g., as shorthaplotypes), and are provided herein as sets of two SNPs.

In some embodiments, the fetal fraction is determined by statistical andapproximation techniques that evaluate the relative contributions ofzygosities from the fetal and maternal genomes by using polymorphicsites to determine the relative contributions. The fetal fraction canalso be determined by electrophoresis methods where certain types ofpolymorphic sites are electrophoretically separated and used to identifyrelative contribution of a polymorphic allele from the fetal genome andrelative contribution of the corresponding polymorphic allele from thematernal genome.

In one embodiment shown in a process flow diagram in FIG. 6, fetalfraction is determined by a method 600 of first obtaining a test samplecomprising a mixture of fetal and maternal nucleic acids in operation610, enriching the mixture of nucleic acids for polymorphic targetnucleic acids in operation 620, sequencing the enriched mixture ofnucleic acids in operation 630, and determining the fetal fraction inthe sample and aneuploidy simultaneously in operation 640.

FIG. 7 shows a process flow diagram for some embodiments. Fetal fractionis determined by: (i) obtaining a maternal plasma sample in operation710, (ii) purifying the cfDNA in the sample in operation 720, (iii)amplifying the polymorphic nucleic acids in operation 730, (iv) usingmassively parallel sequencing methods to sequence the mixture inoperation 740, and (v) calculating the fetal fraction in operation 760.In another embodiment, fetal fraction can be determined by (i) obtaininga maternal plasma sample in operation 710, (ii) purifying the cfDNA inthe sample in operation 720, (iii) amplifying the polymorphic nucleicacids in operation 730, (iv) separating the nucleic acids by size usingelectrophoresis methods in operation 750, and (v) calculating the fetalfraction in sample 770.

In one embodiment shown in process flow diagram in FIG. 8, the fetalfraction is determined by: (i) obtaining a sample comprising a mixtureof fetal and maternal nucleic acids in operation 810, (ii) amplifyingthe sample in operation 820, (iii) enriching the sample by combining theamplified sample with unamplified sample from the original mixture inoperation 830, (iv) purifying the sample in operation 840, and (v)sequencing the sample to determine the fetal fraction using variousmethods in operation 850 to determine the fetal fraction and thepresence or absence of aneuploidy simultaneously in operation 860.

In another embodiment shown in the process flow diagram in FIG. 9, thefetal fraction is determined by: (i) obtaining a sample comprising amixture of fetal and maternal nucleic acids in operation 910, (ii)purifying the sample in operation 920, (iii) amplifying a portion of thesample in operation 930, (iv) enriching the sample by combining theamplified sample with purified unamplified portion of the originalsample from the original mixture in operation 940, and (v) sequencingthe sample in operation 950 to determine the fetal fraction and thepresence or absence of aneuploidy simultaneously in operation 960 usingvarious methods.

In another embodiment shown in the process flow diagram in FIG. 10, thefetal fraction is determined by: (i) obtaining a sample comprising amixture of fetal and maternal nucleic acids in operation 1010, (ii)purifying the sample in operation 1020, (iii) amplifying a first portionof the sample in operation 1040, (iv) preparing a sequencing library ofthe amplified portion of the sample in operation 1050, (v) preparing asequencing library of a second purified unamplified portion of thesample in operation 1030, (vi) enriching the mixture by combining thetwo sequencing libraries in operation 1060, and (vii) sequencing themixture in operation 1070 to determine the fetal fraction and thepresence or absence of aneuploidy simultaneously in operation 1080 usingvarious methods.

In another embodiment, the fetal fraction is determined by: (i)obtaining a sample comprising a mixture of fetal and maternal nucleicacids, (ii) purifying the sample, (iii) amplifying the sample usinglabeled primers, and (iv) sequencing the sample using electrophoresis todetermine the fetal fraction using various methods.

In another embodiment, the fetal fraction is determined by: (i)obtaining a sample comprising a mixture of fetal and maternal nucleicacids, (ii) purifying the sample, (iii) optionally enriching the sampleby amplifying a portion of the sample, and (iv) sequencing the sample todetermine the fetal fraction using various methods.

Purification of the original obtained sample, amplified sample, oramplified and enriched sample, or other nucleic acid samples relevant tothe methods disclosed herein (such as in operations 720, 840, 920, and1020) can be completed by any conventional technique. To separate cfDNAfrom cells, fractionation, centrifugation (e.g., density gradientcentrifugation), DNA-specific precipitation, or high-throughput cellsorting, and/or separation methods can be used. Optionally, the sampleobtained can be fragmented before purification or amplification. If thesample used comprises cfDNA, then fragmentation may not be requiredbecause cfDNA is fragmented in nature, with the fragments frequently ofsize around 150 to 200 bp.

In some of the above-described processes, selective amplification andenrichment is employed to increase the relative amount of nucleic acidfrom regions where polymorphisms are located. A similar result can beachieved by deep sequencing selected regions of the genome, particularlyregions where polymorphisms are located.

Amplification

After obtaining a sample and purifying the sample, a portion of thepurified mixture of fetal and maternal nucleic acids (e.g. cfDNA) isused to amplify a plurality of polymorphic target nucleic acids, eachcomprising a polymorphic site. Amplification of the target nucleic acidsin the mixture of fetal and maternal nucleic acid is accomplished insome implementations by any method that uses PCR (polymerase chainreaction) or variations of the method, including but not limited toasymmetric PCR, helicase-dependent amplification, hot-start PCR, qPCR,solid phase PCR, and touchdown PCR. In some embodiments, the sample canbe partially amplified to facilitate determining fetal fraction. In someembodiments, amplification is not performed. The disclosed methods ofamplifications and other amplification techniques can be used inoperations 730, 820, 930, and 1040.

Amplification of SNPs

A number of nucleic acid primers are available to amplify DNA fragmentscontaining SNPs, and their sequences can be obtained, for example, fromdatabases known by one skilled in the art. Additional primers can alsobe designed, for example, using a method similar to that published byVieux, E. F., Kwok, P-Y and Miller, R. D. in BioTechniques (June 2002)Vol. 32, Supplement: “SNPs: Discovery of Marker Disease,” pp. 28-32.

Sequence-specific primers are selected to amplify target nucleic acids.In one embodiment, target nucleic acids comprising a polymorphic siteare amplified as amplicons. In another embodiment, target nucleic acidscomprising two or more polymorphic sites, e.g. two tandem SNPs, areamplified as amplicons. The single or tandem SNPs are contained inamplified target nucleic acid amplicons of at least about 100 bp. Theprimers used for amplifying the target sequences comprising tandem SNPsare designed to encompass both SNP sites.

Amplification of STRs

Some nucleic acid primers are available to amplify DNA fragmentscontaining STRs and such sequences can be obtained from databases knownby one skilled in the art.

In some embodiments, a portion of the mixture of fetal and maternalnucleic acids is used as a template for amplifying target nucleic acidsthat have at least one STR. A comprehensive listing of references, factsand sequence information on STRs, published PCR primers, commonmultiplex systems, and related population data are compiled in STRBase,which may be accessed via the Internet at cstl.nist.gov/strbase.Sequence information from GenBank® at ncbi.nlm.nih.gov/genbank forcommonly used STR loci is also accessible through STRBase.

STR multiplex systems allow the simultaneous amplification of multiplenonoverlapping loci in a single reaction, substantially increasingthroughput. Because of the high polymorphisms of STRs, most individualswill be heterozygous. STRs can be used in electrophoresis analysis asdescribed further below.

Amplification can also be done using miniSTRs to generate reduced-sizeamplicons to discern STR alleles that are shorter in length. The methodof the disclosed embodiments encompasses determining the fraction offetal nucleic acid in a maternal sample that has been enriched withtarget nucleic acids each comprising one miniSTR comprising quantifyingat least one fetal and one maternal allele at a polymorphic miniSTR,which can be amplified to generate amplicons that are of lengths aboutthe size of the circulating fetal DNA fragments. Any one pair or acombination of two or more pairs of miniSTR primers can be used toamplify at least one miniSTR.

Enrichment

Samples that are enriched may include: a plasma fraction of a bloodsample; a sample of purified cfDNA that is extracted from plasma; asequencing library sample prepared from a purified mixture of fetal andmaternal nucleic acids; and others.

In certain embodiments, the sample comprising the mixture of DNAmolecules is non-specifically enriched for the whole genome prior towhole genome sequencing i.e. whole genome amplification is performedprior to sequencing. Non-specific enrichment of the mixture of nucleicacids may refer to the whole genome amplification of the genomic DNAfragments of the DNA sample that can be used to increase the level ofthe sample DNA prior to identifying polymorphisms by sequencing.Non-specific enrichment can be the selective enrichment of one of thetwo genomes (fetal and maternal) present in the sample.

In other embodiments, the cfDNA in the sample is enriched specifically.Specific enrichment refers to the enrichment of a genomic sample forspecific sequences, e.g. polymorphic target sequence, which isaccomplished by methods that comprise specifically amplifying targetnucleic acid sequences that comprise the polymorphic site.

In other embodiments, the mixture of nucleic acids present in the sampleis enriched for polymorphic target nucleic acids each comprising apolymorphic site. Such enrichment can be used in operation 620.Enrichment of a mixture of fetal and maternal nucleic acids comprisesamplifying target sequences from a portion of nucleic acids contained inthe original maternal sample, and combining part or the entire amplifiedproduct with the remainder of the original maternal sample, such as inoperations 830 and 940.

In yet another embodiment, the sample that is enriched is a sequencinglibrary sample prepared from a purified mixture of fetal and maternalnucleic acids. The amount of amplified product that is used to enrichthe original sample is selected to obtain sufficient sequencinginformation for determining the fetal fraction. At least about 3%, atleast about 5%, at least about 7%, at least about 10%, at least about15%, at least about 20%, at least about 25%, at least about 30% or moreof the total number of sequence tags obtained from sequencing are mappedto determine the fetal fraction.

In one embodiment, in FIG. 10, enrichment includes amplifying the targetnucleic acids that are contained in a portion of an original sample of apurified mixture of fetal and maternal nucleic acids (e.g. cfDNA thathas been purified from a maternal plasma sample) in operation 1040.Similarly, the portion of purified unamplified cfDNA is used to preparea primary sequencing library in operation 1050. In operation 1060, aportion of the target library is combined with the primary librarygenerated from the unamplified mixture of nucleic acids, and the mixtureof fetal and maternal nucleic acids comprised in the two libraries issequenced in operation 1070. The enriched library may include at leastabout 5%, at least about 10%, at least about 15%, at least about 20%, orat least about 25% of the target library. In operation 1080, the datafrom the sequencing runs is analyzed and the simultaneous determinationof the fetal fraction and presence or absence of aneuploidy is made asdescribed in operation 640 of the embodiment depicted in FIG. 6.

Sequence Technology

The enriched mixture of fetal and maternal nucleic acids is sequenced.Sequence information that is needed for the determination of fetalfraction can be obtained using any of the known DNA sequencing methods,many of which are described elsewhere herein. Such sequencing methodsinclude next generation sequencing (NGS), Sanger sequencing, HelicosTrue Single Molecule Sequencing (tSMS™), 454 sequencing (Roche), SOLiDtechnology (Applied Biosystems), Single Molecule Real-Time (SMRT™)sequencing technology (Pacific Biosciences), nanopore sequencing,chemical-sensitive field effect transistor (chemFET) array, HalcyonMolecular's method that uses transmission electron microscopy (TEM), iontorrent single molecule sequencing, sequencing by hybridization, andothers. In some embodiments, massively parallel sequencing is adopted.In one embodiment, Illumina's sequencing-by-synthesis and reversibleterminator-based sequencing chemistry is used. In some embodiments,partial sequencing is used.

The sequenced DNA is mapped to a reference genome. Reference genomes maybe artificial or may be a human reference genome. Such reference genomesinclude: artificial target sequences genome comprising sequences ofpolymorphic target nucleic acids; an artificial SNP reference genome; anartificial STR reference genome; an artificial tandem-STR referencegenome; the human reference genome NCBI36/hg18 sequence, which isavailable on the Internet atgenome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105; andthe human reference genome NCBI36/hg18 sequence and an artificial targetsequences genome, which includes the target polymorphic sequences e.g. aSNP genome. Some mismatch is allowed during the mapping process.

In one embodiment, sequencing information obtained in operation 630 isanalyzed and the simultaneous determination of fetal fraction anddetermination of the presence or absence of aneuploidy is made.

As explained above, a plurality of sequence tags are obtained persample. In some embodiments, at least about 3×106 sequence tags, atleast about 5×106 sequence tags, at least about 8×106 sequence tags, atleast about 10×106 sequence tags, at least about 15×106 sequence tags,at least about 20×106 sequence tags, at least about 30×106 sequencetags, at least about 40×106 sequence tags, or at least about 50×106sequence tags comprising between 20 and 40 bp reads are obtained frommapping the reads to the reference genome per sample. In one embodiment,all the sequence reads are mapped to all regions of the referencegenome. In one embodiment, the tags comprising reads that have beenmapped to all regions e.g. all chromosomes, of the human referencegenome are counted, and the fetal aneuploidy i.e. the over- orunder-representation of a sequence of interest e.g. a chromosome orportion thereof, in the mixed DNA sample is determined, and the tagscomprising reads that are mapped to the artificial target sequencesgenome are counted to determine the fetal fraction. The method does notrequire differentiation between the maternal and fetal genomes.

In one embodiment, the data from the sequencing runs is analyzed and thesimultaneous determination of the fetal fraction and presence or absenceof aneuploidy is made.

Sequencing Libraries

In some embodiments, a portion or all of the amplified polymorphicsequences is used to prepare a sequencing library for sequencing in aparallel fashion as described. In one embodiment, the library isprepared for sequencing-by-synthesis using Illumina's reversibleterminator-based sequencing chemistry. A library can be prepared frompurified cfDNA and includes at least about 10%, at least about 15%, atleast about 20%, at least about 25%, at least about 30%, at least about35%, at least about 40%, at least about 45%, or at least about 50%amplified product.

Sequencing of the library generated by any one of the methods depictedin FIG. 11 provides sequence tags derived from the amplified targetnucleic acids and tags derived from the original unamplified maternalsample. Fetal fraction is calculated from the number of tags mapped toan artificial reference genome.

Calculation of Fetal Fraction As explained, after sequencing therelevant DNA, computational methods can be used to map or align thesequence to a particular gene, chromosome, allele, or other structure. Anumber of computer algorithms exist to align sequences, including,without limitation, BLAST (Altschul et al., 1990), BLITZ (MPsrch)(Sturrock & Collins, 1993), FASTA (Pearson & Lipman, 1988), BOWTIE(Langmead et al., Genome Biology 10:R25.1-R25.10 [2009]), or ELAND(Illumina, Inc., San Diego, Calif., USA). In some embodiments, thesequences of the bins are found in nucleic acid databases known to thosein the art, including, without limitation, GenBank, dbEST, dbSTS, EMBL(the European Molecular Biology Laboratory), and the DDBJ (the DNA DataBank of Japan). BLAST or similar tools can be used to search theidentified sequences against the sequence databases, and search hits canbe used to sort the identified sequences into the appropriate bins.Alternatively, a Bloom filter or similar set membership tester may beemployed to align reads to reference genomes. See U.S. PatentApplication No. 61/552,374 filed Oct. 27, 2011 which is incorporatedherein by reference in its entirety.

As mentioned, the determination of the fetal fraction according to someembodiments, particularly NCNFF techniques, is based on the total numberof tags that map to a first allele and the total number of tags that mapto a second allele at an informative polymorphic site (e.g. a SNP)contained in a reference genome. The informative polymorphic site isidentified by the difference in the allelic sequences and the amount ofeach of the possible alleles. Fetal cfDNA is often present at aconcentration that is <10% of the maternal cfDNA. Thus, the presence ofa minor contribution of an allele to the mixture of fetal and maternalnucleic acids relative to the major contribution of the maternal allelecan be assigned to the fetus. Alleles that are derived from the maternalgenome are herein referred to as major alleles, and alleles that arederived from the fetal genome are herein referred to as minor alleles.Alleles that are represented by similar levels of mapped sequence tagsrepresent maternal alleles. The results of an exemplary multiplexamplification of target nucleic acids comprising SNPs derived from amaternal plasma sample are shown in FIG. 12.

Estimating Fetal Fraction Using Allele Ratios

The relative abundance of fetal cfDNA in the maternal sample can bedetermined as a parameter of the total number of unique sequence tagsmapped to the target nucleic acid sequence on a reference genome foreach of the two alleles of the predetermined polymorphic site. In oneembodiment, the fraction of fetal nucleic acids in the mixture of fetaland maternal nucleic acids is calculated for each of the informativealleles (allele_(x)) as follows:

$\begin{matrix}{\left( {\% \mspace{14mu} {fetal}\mspace{14mu} {fraction}\mspace{14mu} {allele}_{x}} \right) = {\quad{\left\lbrack \frac{\Sigma \left( {{fetal}\mspace{14mu} {sequence}\mspace{14mu} {tags}\mspace{14mu} {for}\mspace{14mu} {allele}_{x}} \right)}{\Sigma \left( {{maternal}\mspace{14mu} {sequence}\mspace{14mu} {tags}\mspace{14mu} {for}\mspace{14mu} {allele}_{x}} \right)} \right\rbrack \times 100}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

and fetal fraction for the sample is calculated as the average of thefetal fraction of all of the informative alleles. Optionally, thefraction of fetal nucleic acids in the mixture of fetal and maternalnucleic acids is calculated for each of the informative alleles(allelex) as follows:

$\begin{matrix}{\left( {\% \mspace{14mu} {fetal}\mspace{14mu} {fraction}\mspace{14mu} {allele}_{x}} \right) = {\quad{\left\lbrack \frac{2 \times {\Sigma \left( {{fetal}\mspace{14mu} {sequence}\mspace{14mu} {tags}\mspace{14mu} {for}\mspace{14mu} {allele}_{x}} \right)}}{\Sigma \left( {{maternal}\mspace{14mu} {sequence}\mspace{14mu} {tags}\mspace{14mu} {for}\mspace{14mu} {allele}_{x}} \right)} \right\rbrack \times 100}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

to compensate for the presence of two fetal alleles, one being masked bythe maternal background.

Estimating Fetal Fraction Using STR Sequences and CapillaryElectrophoresis

Individuals have different lengths of STRs due to different number ofrepeats. Because of the high polymorphism of STRs, most individuals willbe heterozygous i.e. most people will possess two alleles (versions)—oneinherited from each parent—each with a different number of repeats. Thenon-maternally inherited fetal STR sequence will differ in the number ofrepeats from the maternal sequence. Amplification of these STR sequencescan result in one or two major amplification products corresponding tothe maternal alleles (and the maternally inherited fetal allele) and oneminor product corresponding to the non-maternally inherited fetalallele. When sequenced, the collected samples can be correlated with thecorresponding alleles and counted to determine relative fraction byusing Equation 3.

PCR is performed on a purified sample by using fluorescently labeledprimers. The PCR products comprising the STRs can be separated anddetected using manual, semi-automated or automated electrophoresismethods. Semi-automated systems are gel-based and combineelectrophoresis, detection, and analysis into one unit. On asemi-automated system, gel assembly and sample loading are still manualprocesses; however, once samples are loaded onto the gel,electrophoresis, detection and analysis proceed automatically. As thename implies, capillary electrophoresis is carried out in amicrocapillary tube rather than between glass plates. Once samples, gelpolymer, and buffer are loaded onto the instrument, the capillary isfilled with gel polymer and the sample is loaded automatically. Datacollection occurs in “real time” as fluorescently labeled fragmentsmigrate past the detector at a fixed point and can be viewed as they arecollected. The sequence obtained from capillary electrophoresis can bedetected by a program to measure the wavelengths of the fluorescentlabels. The calculation of fetal fraction is based on averaging allinformative markers. Informative markers are identified by the presenceof peaks on the electropherogram that fall within the parameters ofpreset bins for the STRs that are analyzed.

The fraction of the minor allele for any given informative marker iscalculated by dividing the peak height of the minor component by the sumof the peak height for the major component, and the fraction isexpressed as a percent for each informative locus as

$\begin{matrix}{\left( {\% \mspace{14mu} {fetal}\mspace{14mu} {fraction}} \right) = {\quad{\left\lbrack \frac{{peak}\mspace{14mu} {height}\mspace{14mu} {of}\mspace{14mu} {minor}\mspace{14mu} {{allele}(s)}}{\Sigma \left( {{peak}\mspace{14mu} {height}\mspace{14mu} {of}\mspace{14mu} {major}\mspace{14mu} {{allele}(s)}} \right)} \right\rbrack \times 100}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

The fetal fraction for a sample comprising two or more informative STRswould be calculated as the average of the fetal fractions calculated forthe two or more informative markers.

Estimating Fetal Fraction Using Mixture Models

In embodiments disclosed herein, there are up to four different datatypes (the zygosity cases) that make up the minor allele frequency datafor the polymorphisms under consideration.

As indicated in FIG. 13, cases 1 and 2 are the polymorphism cases inwhich the mother is homozygous at a certain allele. In case 1, if thebaby and the mother are both homozygous, the polymorphism is a case 1polymorphism. This situation is typically not particularly interestingbecause the collected data will only have presence of one type of alleleat the analyzed polymorphic site. In case 2, if the mother is homozygousand the baby is heterozygous, the fetal fraction, f, is nominally givenby two times the ratio of the minor allele count to the coverage.Coverage is defined as the total number of reads or tags (both fetal andmaternal) mapping to a particular site of a polymorphism. The equationfor approximating the fetal fraction as a fraction of the fetal andmaternal sample for case 2 is as follows:

$\begin{matrix}{{2 \times {Ratio}\mspace{14mu} {of}\mspace{14mu} {minor}\mspace{14mu} {allele}\mspace{14mu} {count}\mspace{14mu} {to}\mspace{14mu} {coverage}} = {2 \times \left( \frac{{Minor}\mspace{14mu} {allele}\mspace{14mu} {count}}{coverage} \right)}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In case 3, where the mother is heterozygous and the baby is homozygous,the fetal fraction is nominally one minus two times the ratio of theminor allele count to the coverage. The equation for approximating fetalfraction as a fraction of the total number of reads in both the fetaland maternal sample in case 3 is as follows:

$\begin{matrix}{{1 - \left\lbrack {2 \times {Ratio}\mspace{14mu} {of}\mspace{14mu} {minor}\mspace{14mu} {allele}\mspace{14mu} {count}\mspace{14mu} {to}\mspace{14mu} {coverage}} \right\rbrack} = {1 - \left\lbrack {2 \times \left( \frac{{Minor}\mspace{14mu} {allele}\mspace{14mu} {count}}{coverage} \right)} \right\rbrack}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Finally, in case 4 where both the mother and the fetus are heterozygous,the minor allele fraction should always be 0.5, barring error. The fetalfraction cannot be derived for polymorphisms falling into case 4.

Table 7 summarizes an example of estimating fetal fraction usingEquations 4 and 5 if the number of reads of the major allele is 300 andthe number of reads of the minor allele is 200. The coverage would be500.

TABLE 7 Example of estimating fetal fraction using zygosity Case MomBaby Example 1 Homozygous Homozygous Cannot tell 2 HomozygousHeterozygous${2 \times \frac{200}{500}} = {\frac{4}{5} = {0.8\text{*}}}$ 3Heterozygous Homozygous${1 - \left\lbrack {2 \times \frac{200}{500}} \right\rbrack} = {\frac{1}{5} = 0.2}$4 Heterozygous Heterozygous 0.5 if coverage = 500, total number ofreads: allele_(B) = 300 (major), allele_(A) = 200 (minor) *Thiscalculation of fetal fraction is for equation illustration purposes onlyand is not representative of actual fetal fraction values obtained fromthe methods in the disclosed embodiments.

In certain embodiments, a mixture model may be employed to classify acollection of polymorphisms into two or more of the presented zygositycases and concurrently estimate the fetal DNA fraction from mean allelefrequencies for each of these cases. Generally, a mixture model assumesthat a particular collection of data is made up of a mixture ofdifferent types of data, each of which has its own expected distribution(e.g., a normal distribution). The process attempts to find the mean andpossibly other characteristics for each type of data. In embodimentsdisclosed herein, there are up to four different data types (thezygosity cases) that make up the minor allele frequency data for thepolymorphisms under consideration.

In certain embodiments employing mixture models, one or more factorialmoments given by Equation 10 are calculated for the positions wherepolymorphisms are being considered. For example, a factorial momentF_(i) (or a collection of factorial moments) is calculated usingmultiple SNP positions considered in the DNA sequence. As shown inEquation 10 below, each of the various factorial moments F_(i) is asummation over all the various polymorphism positions underconsideration for the ratio of minor allele frequency a_(i) to coveraged_(i) for a given position. As shown in Equation 11 below, thesefactorial moments are also related to the parameters α and p_(i)associated with each of the four zygosity cases described above.Specifically, they related to the probability p_(i) for each of thecases as well as the relative amounts of each of the four cases in thecollection of polymorphisms under consideration given by α. Asexplained, the probability p; is a function of the fraction of fetal DNAin the cell-free DNA in the mother's blood. As explained more fullybelow, by calculating a sufficient number of these factorial moments,the method provides a sufficient number of expressions to solve for allthe unknowns. The unknowns in this case would be the relative amounts ofeach of the four cases in the population of polymorphisms underconsideration as well as the probabilities (and hence fetal DNAfractions) associated with each of these four cases. Similar results canbe obtained using other versions of mixture models. Some versions makeuse of only polymorphisms falling into cases 1 and 2, with polymorphismsfor cases 3 and 4 being filtered by a thresholding technique.

Thus, the factorial moments may be used as part of a mixture model toidentify the probabilities of any combination of the four cases ofzygosity. And, as mentioned, these probabilities, or at least those forcases 2 and 3, are directly related to the fraction of fetal DNA in thetotal cell-free DNA in the mother's blood.

It should also be mentioned that sequencing error given by e may beemployed to reduce the complexity of the system of factorial momentequations that must be solved. In this regard, it should be recognizedthat the sequencing error actually can have any one of four results(corresponding to each of the four possible bases at any givenpolymorphism position).

Let the major allele count at genomic position j be B, the first orderstatistic of counts (number of reads counted) at position j. The majorallele, b, is the corresponding arg max. Subscripts are used when morethan one SNP is being considered. The major allele count is given by:

B≡B _(i) ≡{b _(j) }≡w _(j,i) ⁽¹⁾=max_(i∈{1,2,3,4}) {w _(j,i)}  Equation6

Let the minor allele count at position j be A, the second orderstatistic of counts (i.e. the second highest allele count) at positionj:

A≡A _(i) ≡{a _(j) }=w _(j,i) ⁽²⁾  Equation 7

Coverage is defined as the total number of reads (both fetal andmaternal) mapping to a particular site of a polymorphism. Let coverageat position j be defined as D:

D≡D _(j) ={d _(i) }=A _(j) +B _(j)  Equation 8

In this embodiment, the minor allele frequency A is a sum of four termsas shown in Equation 9. The four heterozygosity cases described suggestthe following binomial mixture model for the distribution of a; minorallele counts in points (a_(i), d_(i)) where d_(i) is the coverage:

A={a _(i)}˜α_(i)Bin(p ₁ ,d _(i))+α₂Bin(p ₂ ,d _(i))+α₃Bin(p ₃ ,d_(i))+α₄Bin(p ₄ ,d _(i))  Equation 9

-   -   where    -   1=α₁+α₂+α₃+α₄    -   m=4

Each term corresponds to one of the four zygosity cases. Each term isthe product of a polymorphism fraction α and a binomial distribution ofthe minor allele frequency. The αs represent the fraction of thepolymorphisms falling into each of the four cases. Each binominaldistribution has an associated probability, p, and coverage, d. Theminor allele probability for case 2, for example, is given by f/2 wheref is the fetal fraction. Various models for relating p_(i) to fetalfraction and sequencing error rates are described below. The parametersα_(i) relate to population specific parameters and the ability to letthese values “float” gives these methods additional robustness withrespect to factors like ethnicity and progeny of the parents.

The disclosed embodiments make use of factorial moments for the allelefrequency data under consideration. As is well known, a distribution'smean is the first moment. It is the expected value of the minor allelefrequency. The variance is the second moment. It is calculated from theexpectation value of the allele frequency squared.

For various heterozygosity cases, Equation 9 above can be solved forfetal fraction. In certain embodiments, fetal fraction is solved throughthe method of factorial moments in which the mixture parameters can beexpressed in terms of moments that can easily be estimated from theobserved data.

The allele frequency data across all polymorphisms may be used tocalculate i-th factorial moment F_(i) (a first factorial moment F₁, asecond factorial moment F₂, etc.) as shown in Equation 10. (SNPs areused for purposes of example only. Other types of polymorphisms may beused as discussed elsewhere herein.) Given n SNP positions, thefactorial moments are defined as follows:

$\begin{matrix}{{F_{1} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\frac{a_{i}}{d_{i}}}}}{F_{2} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\frac{a_{i}\left( {a_{i} - 1} \right)}{d_{i}\left( {d_{i} - 1} \right)}}}}\ldots {F_{j} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\frac{{a_{i}\left( {a_{i} - 1} \right)}\mspace{11mu} \ldots \mspace{11mu} \left( {a_{i} - j + 1} \right)}{{d_{i}\left( {d_{i} - 1} \right)}\left( {d_{i} - j + 1} \right)}}}}} & {{Equation}\mspace{14mu} 10}\end{matrix}$

As indicated by these equations, the factorial moments are summations ofterms over i, the individual polymorphisms in the data set, where thereare n such polymorphisms in the data set. The terms being summed arefunctions of the minor allele counts a_(i), and coverage values d_(i).

Usefully, the factorial moments have relationships with the values ofα_(i) and p_(i) as illustrated in Equation 11. Factorial moments can berelated to the {α_(i), p_(i)} such that

$\begin{matrix}{{F_{1} \approx {\sum\limits_{i = 1}^{m}{\alpha_{i}p_{i}^{1}}}}{F_{2} \approx {\sum\limits_{i = 1}^{m}{\alpha_{i}p_{i}^{2}}}}\ldots {F_{j} \approx {\sum\limits_{i = 1}^{m}{\alpha_{i}p_{i}^{j}}}}\ldots {F_{g} \approx {\sum\limits_{i = 1}^{m}{\alpha_{i}p_{i}^{g}}}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

From the probabilities p_(i), one can determine the fetal fraction, f.For example,

${p_{2} = \frac{f}{2}},{{{and}\mspace{14mu} p_{3}} = {1 - {\frac{f}{2}.}}}$

Thus, the responsible logic can solve a system of equations relating theunknown a and p variables to the factorial moment expressions for minorallele fractions across the multiple polymorphisms under consideration.Of course, there are other techniques for solving the mixture modelswithin the scope of the disclosed embodiments.

A solution can be identified by solving for the {α_(i), p_(i)} in asystem of equations derived from the above relation Equation 8 whenn>2*(number of parameters to be estimated). Obviously, the problembecomes much more difficult mathematically for higher g as more {a_(i),p_(i)} need to be estimated.

It is typically not possible to accurately discriminate between case 1and 2 (or case 3 and 4) data by simple thresholds at lower fetalfractions. Case 1 and 2 data is easily separated from case 3 and 4 databy discriminating at point

$\left( \frac{2A}{D} \right) = T$

where A is the minor allele count and D is the coverage and T is thethreshold. Use of T=0.5 has been found to perform satisfactorily.

Note that the mixture model method employing Equations 10 and 11 makesuse of the data for all polymorphisms but does not separately accountfor the sequencing error. Appropriate methods that separate data for thefirst and second cases from data for the third and fourth cases canaccount for sequencing error.

In further examples, the data set provided to a mixture model containsdata for only case 1 and case 2 polymorphisms. These are polymorphismsfor which the mother is homozygous. A threshold technique may beemployed to remove the case 3 and 4 polymorphisms. For example,polymorphisms with minor allele frequencies greater than a particularthreshold are eliminated before employing the mixture model. Usingappropriately filtered data and factorial moments as reduced toEquations 13 and 14 below, one may calculate the fetal fraction, f, asshown in Equation 15. Note that Equation 13 is a restatement of Equation9 for this implementation of a mixture model. Note also that in thisparticular example, the sequencing error associated with the machinereading is not known. As a consequence, the system of equations mustseparately be solved for the error, e.

FIG. 14 shows a comparison of the results using this mixture model andthe known fetal fraction (x-axis) and estimated fetal fraction (y-axis).If the mixture model perfectly predicted the fetal fraction, the plottedresults would follow the dashed line. Nevertheless, the estimatedfractions are remarkably good, particularly considering that much of thedata was eliminated prior to applying the mixture model.

To further elaborate, several other methods are available for parameterestimation of the model from Equation 7. In some cases, a tractablesolution can be found by setting derivatives to zero of the chi-squaredstatistic. In cases where no easy solution can be found by directdifferentiation, Taylor series expansion of the binomial probabilitydistribution function (PDF) or other approximating polynomials can beeffective. Minimum chi-square estimators are well-known to be efficient.The method of moments solutions from Equation 9 can be used as astarting point for the iteration. The following chi-square estimator canbe used

$\begin{matrix}{{\chi^{2}\left( {\alpha_{i},p_{i}} \right)} = {\sum\limits_{i = 1}^{n}\frac{\left( {P_{i} - {\sum{\alpha_{i}{{Binomial}\left( {p_{i},d_{i}} \right)}}}} \right)^{2}}{{Binomial}\left( {n,p} \right)}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

where P_(i) is the number of points of count i. An alternative methodfrom Le Cam [“On the Asymptotic Theory of Estimation and TestingHypotheses,” Proceedings of the Third Berkeley Symposium on MathematicalStatistics and Probability, Vol. 1 Berkeley Calif.: University of CAPress, 1956, pp. 129-156] uses Ralph-Newton iteration of the likelihoodfunction.

In accordance with another application, a method of resolving mixturemodels involving expectation maximization methods operating on mixturesof approximating Beta distributions is discussed.

Model 1: Cases 1 and 2, Sequencing Error Unknown

Consider a reduced model that only accounts for heterozygosity cases 1and 2. In this case the mixture distribution can be written as

A={a _(i)}˜α₁Bin(e,d _(i))+α₂Bin(f/2,d _(i))  Equation 13

where

1=α₁+α₂

m=4

And the system

F ₁=α₁ e+(1−α₁)(f/2)

F ₂=α₁ e ²+(1−α₁)(f/2)²

F ₃=α₁ e ³+(1−α₁)(f/2)³  Equation 14

is solved for e (sequencing error rate), α (proportion of case 1points), and f (fetal fraction), where the F_(i) are defined as inEquation 10 above. A closed form solution for fetal fraction is chosento be the real solution of

$\begin{matrix}{f \approx \frac{\begin{matrix}{{\left( {F_{1} - 1} \right)F_{2}} \pm} \\{\sqrt{F_{2}}\sqrt{{4F_{1}^{3}} + F_{2} - {3{F_{1}\left( {2 + F_{1}} \right)}F_{2}} + {4F_{2}^{2}}}}\end{matrix}}{2\left( {F_{1}^{2} - F_{2}} \right)}} & {{Equation}\mspace{14mu} 15}\end{matrix}$

that is between 0 and 1.

To gauge the performance of estimators, a simulated data-set ofHardy-Weinberg Equilibrium points (a_(i), d_(i)) was constructed withfetal fraction designed to be (1%, 3%, 5%, 10%, 15%, 20%, and 25%) and aconstant sequencing error rate of 1%. The 1% error rate is the currentlyaccepted rate for the sequencing machines and protocols being used andis consistent with the graph of Illumina Genome analyzer II data shownin FIG. 15. Equation 15 was applied to the data and found, with theexception of a four point bias upwards, general agreement with the“known” fetal fraction. Interestingly, the sequencing error rate, e, isestimated to be just above 1%.

Model 2: Cases 1 and 2, Sequencing Error Known

In the next mixture model example, thresholding or another filteringtechnique is again employed to remove data for polymorphisms fallinginto cases 3 and 4. However in this case, the sequencing error is known.This simplifies the resulting expression for fetal fraction, f, as shownin Equation 16. FIG. 16 shows that this version of a mixture modelprovided improved results compared to the approach employed withEquation 15. Let the sequencing machine error rate be e in thesubsequent equations.

A similar approach is shown in Equations 17 and 18. This approachrecognizes that only some sequencing errors add to the minor allelecount. Rather, only one in every four sequencing errors should increasethe minor allele count. FIG. 17 shows remarkably good agreement betweenthe actual and estimated fetal fractions using this technique.

Since the sequencing error rate of the machines used is known to a greatextent, the bias and complexity of calculations can be reduced byeliminating e as a variable to be solved. Thus we obtain the system ofequations

F ₁=α₁ e+(1−α₁)(f/2)

F ₂=α₁ e ²+(1−α₁)(f/2)²  Equation 16

for fetal fraction f to obtain the solution:

$f \approx \frac{2\left( {{eF}_{1} - F_{2}} \right)}{\left( {e - F_{1}} \right)}$

FIG. 16 shows that using the machine error rate as a known parameterreduces the upward bias by a point.

Model 3: Cases 1 and 2, Sequencing Error Known, Improved Error Models

To ameliorate bias in the model, we expanded the error model of theabove equations to account for the fact that not every sequencing errorevent will add to minor allele count A=a_(i) in heterozygosity case 1.Furthermore, we allow for the fact that sequencing error events maycontribute to heterozygosity case 2 counts. Hence we determine fetalfraction f by solving for the following system of factorial momentrelations:

$\begin{matrix}{{F_{1} = {{\alpha_{1}{e/4}} + {\left( {1 - \alpha_{1}} \right)\left( {e + {f/2}} \right)}}}{F_{2} = {{\alpha_{1}\left( \frac{e}{4} \right)}^{2} + {\left( {1 - \alpha_{1}} \right)\left( {e + {f/2}} \right)^{2}}}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$

The solution to the system is then:

$\begin{matrix}{f \approx \frac{{- 2}\left( {e^{2} - {5{eF}_{1}} + {4F_{2}}} \right)}{\left( {e - {4\; F_{1}}} \right)}} & {{Equation}\mspace{14mu} 18}\end{matrix}$

FIG. 17 shows that simulated data using the machine error rate as aknown parameter, enhancing the case 1 and 2 error models, greatlyreduces the upward bias to less than a point for fetal fraction below0.2.

Using Fetal Fraction to Classify Affected Samples

In certain embodiments, fetal fraction estimates are employed to furthercharacterize affected samples. In some cases, fetal fraction estimatesallow an affected sample to be classified as a mosaic, a completeaneuploidy or a partial aneuploidy. One computer-implemented approach toobtaining this information is depicted with respect to the flowchart ofFIG. 18. This and related methods may be performed to providesimultaneous estimation of fetal fraction, determination of CNVs andclassification of the CNVs. In other words, the same tags may beemployed to perform any of three of these functions.

In order to use this method, two modes of estimating fetal fraction areemployed. One mode produces a NCNFF value and the other mode produces aCNFF value. As explained, the CNFF value is obtained using a techniquethat relies on a chromosome or chromosome segment determined to possessa copy number variation. It need not rely on polymorphisms to calculatefetal fraction. An example of a non-polymorphic technique to calculatefetal fraction is described below in Example 17, which assumes thatthere is a duplication or deletion of a full chromosome and employs thefollowing expression:

ff _((i))=2*NCV _(jA) CV _(jU)  Equation 25

where j represents the identify of an aneuploidy chromosome and CVrepresents the coefficient of variation obtained from the qualifiedsamples used to determine the mean and standard deviation in theexpression for NCV.

The NCNFF value is obtained using a technique that relies on achromosome or chromosome segment that does not have a copy numbervariation. Stated another way, the NCN fetal fraction is determined by atechnique that reliably determines fetal fraction assuming normal ploidyof the portion of the genome used to calculate fetal fraction. The CNfetal fraction is determined by a technique that assumes the sampleunder consideration has a form of aneuploidy. The CNV of the affectedchromosome or chromosome segment is used to calculate the CN fetalfraction. Techniques for its calculation are presented below.

By comparing the estimated value of NCN fetal fraction against theestimated value of CN fetal fraction, a method can determine the type ofaneuploidy that may be present in a sample. Basically, if the NCN fetalfraction and the CN fetal fraction values match, the ploidy assumptionin the techniques for estimating CN fetal fraction can be considered tobe true. For example, if the method of calculating CN fetal fractionassumes that the sample has a complete chromosomal aneuploidy exhibitingeither a single additional copy of a chromosome or a single deletion ofa chromosome, and the NCN fetal fraction value matches the CN fetalfraction value, then the method may conclude that the sample exhibits acomplete chromosomal aneuploidy. The basis for making the assumption isdescribed in more detail below.

The NCN fetal fraction may be determined by various techniques. In someembodiments, the NCN fetal fraction is estimated using selectedpolymorphisms in a reference genome. Examples of these techniques weredescribed above. In other embodiments, NCN fetal fraction is determinedusing the relative amount of X chromosome or Y chromosome (e.g., thechromosome dose of such chromosome) from a sample containing DNA from apregnant mother carrying a son. The son's genome will not include asecond copy of the X chromosome. Knowing this, the relative amount of Xchromosome DNA can be used to provide a NCN value of fetal fraction.

Turning to the flowchart 1800 of FIG. 18, a NCN fetal fraction estimate1802 and a CN fetal fraction estimate 1804 are compared. If they matchas indicated at block 1806 the process is concluded and it is determinedthat the assumption implicit in the technique for estimating CN fetalfraction is true. In various embodiments, this assumption is that atrisomy or monosomy is present in one of the chromosomes of the fetus.

If, on the other hand, the comparison indicates that the two values offetal fraction do not match (condition 1808) and in fact the estimationof CN fetal fraction is less than the NCN fetal fraction, then a secondphase of the method is executed as indicated at block 1810.

In this second phase, the method determines whether the sample containsa partial aneuploidy or a mosaic. Further, if the sample includes apartial aneuploidy, the method determines where on the aneuploidchromosome the aneuploidy resides. In certain embodiments, this isaccomplished by first binning the affected chromosome into multipleblocks. In one example, each block is about 1 million base pairs inlength. Of course, other block lengths may be used such as about 1 kb,about 10 kb, about 100 kb, etc. The blocks do not overlap and span muchor all of the length of the chromosome. The blocks or bins are comparedto one another and this comparison provides insight about the condition.In one approach, for each block or bin, the mapped tags are counted andoptionally converted to bin doses. These counts or bin doses willindicate which, if any of the bins or blocks is aneuploid. As part ofthe analysis of individual bins, it may be appropriate to normalize theinformation from each bin to account for inter-bin variations such asG-C content. The resulting normalized bins may be referred to as NBV fornormalizing bin values; NBV is an example of a chromosome segment thatis normalized to tags mapped to normalizing segments of GC content ofsegments with similar GC content (as in Example 19 below). In someembodiments, the fetal fraction is calculated for each bin and theindividual values of fetal fraction values are compared. This sequentialanalysis of each bin is depicted in block 1812 of FIG. 18. If any of thebins or blocks is identified as having aneuploidy (by considering tagdensities, fetal fractions or other information), the method determinesthat the sample comprises a partial aneuploidy and additionallylocalizes the aneuploidy with the bin in which the tag countsufficiently deviates from an expected value. See block 1814.

If, however, when analyzing the individual the ends of the chromosomeunder consideration, the method does not identify any region of thechromosome exhibiting aneuploidy, the method determines that the samplecontains a mosaic. See block 1816.

Calculating and Comparing True Fetal Fraction Using Polymorphisms e.g.SNPs on the Affected Sample's Chromosome of Interest and on a ChromosomeKnown not to be Aneuploid (e.g. Chromosome X) to Determine the Presenceor Absence of Complete or Partial Aneuploidies in Male Fetuses

As explained, the fetal fraction (FF) that is determined usinginformative polymorphic sequences e.g. informative SNPs, can be used todistinguish complete chromosomal aneuploidies from partial aneuploidies.

The presence or absence of an aneuploidy, whether partial or complete,can be determined from the value of fetal fraction that is determinedusing polymorphic target sequences present on a chromosome of interestand compared to the value of the fetal fraction determined usingpolymorphic target sequences present on a different chromosome in thesample. In samples where the fetus is a male, FF can be determined on achromosome of interest, and compared to FF that is determined forchromosome X in the same sample. For example, given a maternal samplefrom a mother carrying a male fetus with trisomy 21, polymorphicsequences e.g. sequences comprising at least one informative SNP, areselected for being present on chromosome 21 and on chromosome X; thepolymorphic target sequences are amplified, and sequenced, and the fetalfraction is determined as described elsewhere herein.

Given that the fetal fraction is proportional to the amount of a fetalchromosome in a sample, the fetal fraction determined using polymorphicsequences present on a trisomic chromosome in a maternal sample will be1+1/2 times the fetal fraction determined using polymorphic sequences ona chromosome known not to be aneuploid e.g. chromosome X in a malefetus, in the same maternal sample. For example, in a normal sample,when fetal fraction is determined using a panel of polymorphisms onchromosome 21 (FF₂₁), and fetal fraction is determined using a panel ofpolymorphisms on chromosome X (FF_(X)), which is known to be unaffectedin a male fetus, then FF₂₁=FF_(X). However, if the fetus is trisomic forchromosome 21, then, the fetal fraction for a trisomic chromosome 21(FF₂₁) will equal one and a half times the fetal fraction of chromosomeX (FF_(X)) in the same sample (FF₂₁=1.5. FF_(X)). It follows that ifFF₂₁<FF_(X), the analysis logic can conclude that there is a partialdeletion of chromosome 21 and/or the presence of masaicism. IfFF₂₁>FF_(X), the analysis logic can conclude that there is an increasein a portion of chromosome 21 e.g. a partial duplication ormultiplication, or of a complete duplication of chromosome 21 that wasnot accounted for in the technique employed to calculate fetal fractionfrom chromosome 21. The difference between the two outcomes can beresolved as a partial duplication will result in a FF that is <1.5.FF_(X). Alternatively, partial duplications, deletions or presence ofmosaicism can be determined by e.g. increasing the number of polymorphicsequences on chromosome 21 to obtain multiple FF values along the lengthof the chromosome, such that a localized presence of a double ormultiple value for the FF indicates an increase in a portion of thechromosome. Alternatively, as would be the case for a mosaic sample, theFF determined from the polymorphic sequences remains unchangedthroughout the length of the chromosome, indicating an overall increasein the amount of the complete chromosome, but which increase is lessthan that for FF_(X), as described above. In cases where there is a lossof an entire chromosome e.g. monosomy X, then the FF_(monosomy)=½FF_(x).Fetal fraction values obtained from informative polymorphic sequencescan be used in combination with sequence doses and their normalized dosevalues e.g. NCV, NSV, to confirm the presence of a complete aneuploidy.

Calculating Fetal Fraction from Chromosome Doses of Aneuploid Sequences

NCVs for the chromosome of interest were calculated according to theequation

$\begin{matrix}{{NCV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

where {circumflex over (μ)}j and {circumflex over (σ)}_(j) are theestimated mean and standard deviation, respectively, for the j-thchromosome dose in a set of qualified samples, and x_(ij) is theobserved j-th chromosome dose for test sample i.

In general, the chromosome dose for trisomies will increase inproportion to the fetal fraction (ff). Therefore, the ff for achromosome dose in a sample containing a trisomic chromosome willincrease in proportion to the fetal fraction

$\begin{matrix}{R_{jA} = {\left( {1 + \frac{ff}{2}} \right)R_{jU}}} & {{Equation}\mspace{14mu} 20}\end{matrix}$

where R_(jA) is the chromosome dose (x_(ij)) for chromosome j in anaffected sample i, ff is the expected fetal fraction in the unaffected(qualified) sample U, and R_(jU) is the chromosome dose in theunaffected sample. The factor “2” is included based on the assumptionthat there is one extra copy of the chromosome of interest. If other adifferent assumption is made (e.g., there this a partial duplication ofthe chromosome of interest, then the factor “2” does not representreality. Substituting the chromosome dose R_(A) in equation 19

$\begin{matrix}{{NCV}_{jA} = \frac{R_{j\; A} - \overset{\_}{R_{jU}}}{\sigma_{jU}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

where R_(jU) is the equivalent of {circumflex over (μ)}_(j), and σ_(jU)is the equivalent {circumflex over (σ)}_(j); ff is solved as follows:

$\begin{matrix}{{NCV}_{jA} = \frac{{\left( {1 + \frac{ff}{2}} \right)\overset{\_}{R_{jU}}} - \overset{\_}{R_{jU}}}{\sigma_{jU}}} & {{Equation}\mspace{14mu} 22} \\{{NCV}_{jA} = \frac{\left( \frac{ff}{2} \right)\overset{\_}{R_{jU}}}{\sigma_{jU}}} & {{Equation}\mspace{14mu} 23} \\{{NCV}_{jA} = \frac{ff}{2\; {CV}_{jU}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$

Therefore, the percent “ff_((i))” can be determined for any chromosomeas:

ff _((i))=2*NCV _(jA) CV _(jU)  Equation 25

Using Fetal Fraction to Resolve No-Calls

The ability of determining significant differences in the representationof one or more sequences present in a mixture of two genomes ispredicated on the relative contribution of sequences by the first genomerelative to the contribution of the second genome. For example,noninvasive prenatal diagnosis using cfDNA in a maternal sample ischallenging because only a small portion of the DNA sample is derivedfrom the fetus. For prenatal diagnostic assays, the background ofmaternal DNA provides a practical limit on sensitivity, and thereforethe fraction of fetal DNA present in the maternal sample is an importantparameter. The sensitivity of fetal aneuploidy detection by counting DNAmolecules depends on the fetal DNA fraction and the number of moleculesthat are counted.

Typically, about 1% of maternal test samples that analyzed for fetalaneuploidies by massively parallel sequencing are “no-call” samples forwhich insufficient sequencing information e.g. number of fetal sequencetags, precludes a confident determination of the presence or absence oneor more fetal aneuploidies in the maternal sample. The “no-call”determination can result from levels of fetal cfDNA that are too lowrelative to the level of the maternal contribution to the sample toprovide sequencing information that distinguishes the aneuploid samplefrom the sequencing information determined in qualified samples. Todetermine whether the “no-call” sample is or is not an aneuploid sample,fetal fraction determined empirically, and/or derived from, e.g., NVCvalues and used to confirm or deny the presence of chromosomalaneuploidies. As described elsewhere herein, ff can be used tocharacterize the type of aneuploidy present in a test sample. Forexample, for thresholds setting the “no-call” zone between 2.5 and 4 NCVvalues, a test sample having an NCV bordering the 4 NCV threshold andshown to have a low (e.g. less than 3%) fetal fraction is likely to bean affected sample. Conversely, a test sample having an NCV borderingthe 2.5 NCV threshold and shown to have a high (e.g. greater than 40%)fetal fraction is likely to be an unaffected sample. Resolving the“no-call” samples can rely on one determination of fetal fraction.Preferably, the fetal fraction is determined according to two or moredifferent methods, or from using NCVs determined from two or moredifferent chromosomes in the sample using the same method. Similarly,fetal fraction can be used to assess whether samples with NCVsmarginally greater than 4 or marginally smaller than NCVs of 2.5, may befalse positive or false negative calls, respectively.

Apparatus and Systems for Determining CNV

Analysis of the sequencing data and the diagnosis derived therefrom aretypically performed using various computer executed algorithms andprograms. Therefore, certain embodiments employ processes involving datastored in or transferred through one or more computer systems or otherprocessing systems. Embodiments of the invention also relate toapparatus for performing these operations. This apparatus may bespecially constructed for the required purposes, or it may be ageneral-purpose computer (or a group of computers) selectively activatedor reconfigured by a computer program and/or data structure stored inthe computer. In some embodiments, a group of processors performs someor all of the recited analytical operations collaboratively (e.g., via anetwork or cloud computing) and/or in parallel. A processor or group ofprocessors for performing the methods described herein may be of varioustypes including microcontrollers and microprocessors such asprogrammable devices (e.g., CPLDs and FPGAs) and non-programmabledevices such as gate array ASICs or general purpose microprocessors.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude program instructions and/or data (including data structures) forperforming various computer-implemented operations. Examples ofcomputer-readable media include, but are not limited to, semiconductormemory devices, magnetic media such as disk drives, magnetic tape,optical media such as CDs, magneto-optical media, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and random access memory (RAM).The computer readable media may be directly controlled by an end user orthe media may be indirectly controlled by the end user. Examples ofdirectly controlled media include the media located at a user facilityand/or media that are not shared with other entities. Examples ofindirectly controlled media include media that is indirectly accessibleto the user via an external network and/or via a service providingshared resources such as the “cloud.” Examples of program instructionsinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter.

In various embodiments, the data or information employed in thedisclosed methods and apparatus is provided in an electronic format.Such data or information may include reads and tags derived from anucleic acid sample, counts or densities of such tags that align withparticular regions of a reference sequence (e.g., that align to achromosome or chromosome segment), reference sequences (includingreference sequences providing solely or primarily polymorphisms),chromosome and segment doses, calls such as aneuploidy calls, normalizedchromosome and segment values, pairs of chromosomes or segments andcorresponding normalizing chromosomes or segments, counselingrecommendations, diagnoses, and the like. As used herein, data or otherinformation provided in electronic format is available for storage on amachine and transmission between machines. Conventionally, data inelectronic format is provided digitally and may be stored as bits and/orbytes in various data structures, lists, databases, etc. The data may beembodied electronically, optically, etc.

In one embodiment, the invention provides a computer program product forgenerating an output indicating the presence or absence of an aneuploidye.g. a fetal aneuploidy or cancer, in a test sample. The computerproduct may contain instructions for performing any one or more of theabove-described methods for determining a chromosomal anomaly. Asexplained, the computer product may include a non-transitory and/ortangible computer readable medium having a computer executable orcompilable logic (e.g., instructions) recorded thereon for enabling aprocessor to determine chromosome doses and, in some cases, whether afetal aneuploidy is present or absent. In one example, the computerproduct comprises a computer readable medium having a computerexecutable or compilable logic (e.g., instructions) recorded thereon forenabling a processor to diagnose a fetal aneuploidy comprising: areceiving procedure for receiving sequencing data from at least aportion of nucleic acid molecules from a maternal biological sample,wherein said sequencing data comprises a calculated chromosome and/orsegment dose; computer assisted logic for analyzing a fetal aneuploidyfrom said received data; and an output procedure for generating anoutput indicating the presence, absence or kind of said fetalaneuploidy.

The sequence information from the sample under consideration may bemapped to chromosome reference sequences to identify a number ofsequence tags for each of any one or more chromosomes of interest and toidentify a number of sequence tags for a normalizing segment sequencefor each of said any one or more chromosomes of interest. In variousembodiments, the reference sequences are stored in a database such as arelational or object database, for example.

It should be understood that it is not practical, or even possible inmost cases, for an unaided human being to perform the computationaloperations of the methods disclosed herein. For example, mapping asingle 30 bp read from a sample to any one of the human chromosomesmight require years of effort without the assistance of a computationalapparatus. Of course, the problem is compounded because reliableaneuploidy calls generally require mapping thousands (e.g., at leastabout 10,000) or even millions of reads to one or more chromosomes.

The methods disclosed herein can be performed using a computer-readablemedium having stored thereon computer-readable instructions for carryingout a method for identifying any CNV e.g. chromosomal or partialaneuploidies. Thus, in one embodiment, the invention provides acomputer-readable medium having stored thereon computer-readableinstructions for carrying out a method for identifying complete andpartial chromosomal aneuploidies e.g. fetal aneuploidies. Suchinstructions may include, for example, instructions for (a) obtainingand/or storing in a computer readable medium, at least temporarily,sequence information for fetal and maternal nucleic acids in a sample;(b) using the stored sequence information to computationally identify anumber of sequence tags from the mixture of fetal and maternal nucleicacids for each of any one or more chromosomes of interest selected fromchromosomes 1-22, X and Y, and to identify a number of sequence tags forat least one normalizing chromosome sequence for each of the one or morechromosomes of interest; and (c) computationally calculating, using thenumber of sequence tags identified for each of the one or morechromosomes of interest and the number of sequence tags identified foreach normalizing chromosome sequence, a single chromosome dose for eachof the chromosomes of interest. These instructions may be executed usingone or more appropriately designed or configured processors. Theinstructions may additionally include comparing each of the chromosomedoses to associated threshold values, and thereby determining thepresence or absence of any four or more partial or complete differentfetal chromosomal aneuploidies in the sample. As explained above, thereare numerous variations on this process. All such variations can beimplemented in using processing and storage features as described here.

In some embodiments, the instructions may further include automaticallyrecording information pertinent to the method such as chromosome dosesand the presence or absence of a fetal chromosomal aneuploidy in apatient medical record for a human subject providing the maternal testsample. The patient medical record may be maintained by, for example, alaboratory, physician's office, a hospital, a health maintenanceorganization, an insurance company, or a personal medical recordwebsite. Further, based on the results of the processor-implementedanalysis, the method may further involve prescribing, initiating, and/oraltering treatment of a human subject from whom the maternal test samplewas taken. This may involve performing one or more additional tests oranalyses on additional samples taken from the subject.

Disclosed methods can also be performed using a computer processingsystem which is adapted or configured to perform a method foridentifying any CNV e.g. chromosomal or partial aneuploidies. Thus, inone embodiment, the invention provides a computer processing systemwhich is adapted or configured to perform a method as described herein.In one embodiment, the apparatus comprises a sequencing device adaptedor configured for sequencing at least a portion of the nucleic acidmolecules in a sample to obtain the type of sequence informationdescribed elsewhere herein. The apparatus may also include componentsfor processing the sample. Such components are described elsewhereherein.

Sequence or other data, can be input into a computer or stored on acomputer readable medium either directly or indirectly. In oneembodiment, a computer system is directly coupled to a sequencing devicethat reads and/or analyzes sequences of nucleic acids from samples.Sequences or other information from such tools are provided viainterface in the computer system. Alternatively, the sequences processedby system are provided from a sequence storage source such as a databaseor other repository. Once available to the processing apparatus, amemory device or mass storage device buffers or stores, at leasttemporarily, sequences of the nucleic acids. In addition, the memorydevice may store tag counts for various chromosomes or genomes, etc. Thememory may also store various routines and/or programs for analyzing thepresenting the sequence or mapped data. Such programs/routines mayinclude programs for performing statistical analyses, etc.

In one example, a user provides a sample into a sequencing apparatus.Data is collected and/or analyzed by the sequencing apparatus which isconnected to a computer. Software on the computer allows for datacollection and/or analysis. Data can be stored, displayed (via a monitoror other similar device), and/or sent to another location. The computermay be connected to the internet which is used to transmit data to ahandheld device utilized by a remote user (e.g., a physician, scientistor analyst). It is understood that the data can be stored and/oranalyzed prior to transmittal. In some embodiments, raw data iscollected and sent to a remote user or apparatus that will analyzeand/or store the data. Transmittal can occur via the internet, but canalso occur via satellite or other connection. Alternately, data can bestored on a computer-readable medium and the medium can be shipped to anend user (e.g., via mail). The remote user can be in the same or adifferent geographical location including, but not limited to abuilding, city, state, country or continent.

In some embodiments, the methods also include collecting data regardinga plurality of polynucleotide sequences (e.g., reads, tags and/orreference chromosome sequences) and sending the data to a computer orother computational system. For example, the computer can be connectedto laboratory equipment, e.g., a sample collection apparatus, anucleotide amplification apparatus, a nucleotide sequencing apparatus,or a hybridization apparatus. The computer can then collect applicabledata gathered by the laboratory device. The data can be stored on acomputer at any step, e.g., while collected in real time, prior to thesending, during or in conjunction with the sending, or following thesending. The data can be stored on a computer-readable medium that canbe extracted from the computer. The data collected or stored can betransmitted from the computer to a remote location, e.g., via a localnetwork or a wide area network such as the internet. At the remotelocation various operations can be performed on the transmitted data asdescribed below.

Among the types of electronically formatted data that may be stored,transmitted, analyzed, and/or manipulated in systems, apparatus, andmethods disclosed herein are the following:

-   -   Reads obtained by sequencing nucleic acids in a test sample    -   Tags obtained by aligning reads to a reference genome or other        reference sequence or sequences    -   The reference genome or sequence    -   Sequence tag density—Counts or numbers of tags for each of two        or more regions (typically chromosomes or chromosome segments)        of a reference genome or other reference sequences    -   Identities of normalizing chromosomes or chromosome segments for        particular chromosomes or chromosome segments of interest    -   Doses for chromosomes or chromosome segments (or other regions)        obtained from chromosomes or segments of interest and        corresponding normalizing chromosomes or segments    -   Thresholds for calling chromosome doses as either affected,        non-affected, or no call    -   The actual calls of chromosome doses    -   Diagnoses (clinical condition associated with the calls)    -   Recommendations for further tests derived from the calls and/or        diagnoses    -   Treatment and/or monitoring plans derived from the calls and/or        diagnoses

These various types of data may be obtained, stored transmitted,analyzed, and/or manipulated at one or more locations using distinctapparatus. The processing options span a wide spectrum. At one end ofthe spectrum, all or much of this information is stored and used at thelocation where the test sample is processed, e.g., a doctor's office orother clinical setting. In other extreme, the sample is obtained at onelocation, it is processed and optionally sequenced at a differentlocation, reads are aligned and calls are made at one or more differentlocations, and diagnoses, recommendations, and/or plans are prepared atstill another location (which may be a location where the sample wasobtained).

In various embodiments, the reads are generated with the sequencingapparatus and then transmitted to a remote site where they are processedto produce aneuploidy calls. At this remote location, as an example, thereads are aligned to a reference sequence to produce tags, which arecounted and assigned to chromosomes or segments of interest. Also at theremote location, the counts are converted to doses using associatednormalizing chromosomes or segments. Still further, at the remotelocation, the doses are used to generate aneuploidy calls.

Among the processing operations that may be employed at distinctlocations are the following:

-   -   Sample collection    -   Sample processing preliminary to sequencing    -   Sequencing    -   Analyzing sequence data and deriving aneuploidy calls    -   Diagnosis    -   Reporting a diagnosis and/or a call to patient or health care        provider    -   Developing a plan for further treatment, testing, and/or        monitoring    -   Executing the plan    -   Counseling

Any one or more of these operations may be automated as describedelsewhere herein. Typically, the sequencing and the analyzing ofsequence data and deriving aneuploidy calls will be performedcomputationally. The other operations may be performed manually orautomatically.

Examples of locations where sample collection may be performed includehealth practitioners' offices, clinics, patients' homes (where a samplecollection tool or kit is provided), and mobile health care vehicles.Examples of locations where sample processing prior to sequencing may beperformed include health practitioners' offices, clinics, patients'homes (where a sample processing apparatus or kit is provided), mobilehealth care vehicles, and facilities of aneuploidy analysis providers.Examples of locations where sequencing may be performed include healthpractitioners' offices, clinics, health practitioners' offices, clinics,patients' homes (where a sample sequencing apparatus and/or kit isprovided), mobile health care vehicles, and facilities of aneuploidyanalysis providers. The location where the sequencing takes place may beprovided with a dedicated network connection for transmitting sequencedata (typically reads) in an electronic format. Such connection may bewired or wireless and have and may be configured to send the data to asite where the data can be processed and/or aggregated prior totransmission to a processing site. Data aggregators can be maintained byhealth organizations such as Health Maintenance Organizations (HMOs).

The analyzing and/or deriving operations may be performed at any of theforegoing locations or alternatively at a further remote site dedicatedto computation and/or the service of analyzing nucleic acid sequencedata. Such locations include for example, clusters such as generalpurpose server farms, the facilities of an aneuploidy analysis servicebusiness, and the like. In some embodiments, the computational apparatusemployed to perform the analysis is leased or rented. The computationalresources may be part of an internet accessible collection of processorssuch as processing resources colloquially known as the cloud. In somecases, the computations are performed by a parallel or massivelyparallel group of processors that are affiliated or unaffiliated withone another. The processing may be accomplished using distributedprocessing such as cluster computing, grid computing, and the like. Insuch embodiments, a cluster or grid of computational resourcescollective form a super virtual computer composed of multiple processorsor computers acting together to perform the analysis and/or derivationdescribed herein. These technologies as well as more conventionalsupercomputers may be employed to process sequence data as describedherein. Each is a form of parallel computing that relies on processorsor computers. In the case of grid computing these processors (oftenwhole computers) are connected by a network (private, public, or theInternet) by a conventional network protocol such as Ethernet. Bycontrast, a supercomputer has many processors connected by a localhigh-speed computer bus.

In certain embodiments, the diagnosis (e.g., the fetus has Downssyndrome or the patient has a particular type of cancer) is generated atthe same location as the analyzing operation. In other embodiments, itis performed at a different location. In some examples, reporting thediagnosis is performed at the location where the sample was taken,although this need not be the case. Examples of locations where thediagnosis can be generated or reported and/or where developing a plan isperformed include health practitioners' offices, clinics, internet sitesaccessible by computers, and handheld devices such as cell phones,tablets, smart phones, etc. having a wired or wireless connection to anetwork. Examples of locations where counseling is performed includehealth practitioners' offices, clinics, internet sites accessible bycomputers, handheld devices, etc.

In some embodiments, the sample collection, sample processing, andsequencing operations are performed at a first location and theanalyzing and deriving operation is performed at a second location.However, in some cases, the sample collection is collected at onelocation (e.g., a health practitioner's office or clinic) and the sampleprocessing and sequencing is performed at a different location that isoptionally the same location where the analyzing and deriving takeplace.

In various embodiments, a sequence of the above-listed operations may betriggered by a user or entity initiating sample collection, sampleprocessing and/or sequencing. After one or more these operations havebegun execution the other operations may naturally follow. For example,the sequencing operation may cause reads to be automatically collectedand sent to a processing apparatus which then conducts, oftenautomatically and possibly without further user intervention, thesequence analysis and derivation of aneuploidy operation. In someimplementations, the result of this processing operation is thenautomatically delivered, possibly with reformatting as a diagnosis, to asystem component or entity that processes reports the information to ahealth professional and/or patient. As explained such information canalso be automatically processed to produce a treatment, testing, and/ormonitoring plan, possibly along with counseling information. Thus,initiating an early stage operation can trigger an end to end sequencein which the health professional, patient or other concerned party isprovided with a diagnosis, a plan, counseling and/or other informationuseful for acting on a physical condition. This is accomplished eventhough parts of the overall system are physically separated and possiblyremote from the location of, e.g., the sample and sequence apparatus.

FIG. 19 shows one implementation of a dispersed system for producing acall or diagnosis from a test sample. A sample collection location 01 isused for obtaining a test sample from a patient such as a pregnantfemale or a putative cancer patient. The samples then provided to aprocessing and sequencing location 03 where the test sample may beprocessed and sequenced as described above. Location 03 includesapparatus for processing the sample as well as apparatus for sequencingthe processed sample. The result of the sequencing, as describedelsewhere herein, is a collection of reads which are typically providedin an electronic format and provided to a network such as the Internet,which is indicated by reference number 05 in FIG. 19.

The sequence data is provided to a remote location 07 where analysis andcall generation are performed. This location may include one or morepowerful computational devices such as computers or processors. Afterthe computational resources at location 07 have completed their analysisand generated a call from the sequence information received, the call isrelayed back to the network 05. In some implementations, not only is acall generated at location 07 but an associated diagnosis is alsogenerated. The call and or diagnosis are then transmitted across thenetwork and back to the sample collection location 01 as illustrated inFIG. 19. As explained, this is simply one of many variations on how thevarious operations associated with generating a call or diagnosis may bedivided among various locations. One common variant involves providingsample collection and processing and sequencing in a single location.Another variation involves providing processing and sequencing at thesame location as analysis and call generation.

FIG. 20 elaborates on the options for performing various operations atdistinct locations. In the most granular sense depicted in FIG. 20, eachof the following operations is performed at a separate location: samplecollection, sample processing, sequencing, read alignment, calling,diagnosis, and reporting and/or plan development.

In one embodiment that aggregates some of these operations, sampleprocessing and sequencing are performed in one location and readalignment, calling, and diagnosis are performed at a separate location.See the portion of FIG. 20 identified by reference character A. Inanother implementation, that is identified by character B in FIG. 20,sample collection, sample processing, and sequencing are all performedat the same location. In this implementation, read alignment and callingare performed in a second location. Finally, diagnosis and reportingand/or plan development are performed in a third location. In theimplementation depicted by character C in FIG. 20, sample collection isperformed at a first location, sample processing, sequencing, readalignment, calling, and diagnosis are all performed together at a secondlocation, and reporting and/or plan development are performed at a thirdlocation. Finally, in the implementation labeled D in FIG. 20, samplecollection is performed at a first location, sample processing,sequencing, read alignment, and calling are all performed at a secondlocation, and diagnosis and reporting and/or plan management areperformed at a third location.

In one embodiment, the invention provides a system for use indetermining the presence or absence of any one or more differentcomplete fetal chromosomal aneuploidies in a maternal test samplecomprising fetal and maternal nucleic acids, the system including asequencer for receiving a nucleic acid sample and providing fetal andmaternal nucleic acid sequence information from the sample; a processor,and a machine readable storage medium comprising instructions forexecution on said processor, the instructions comprising:

(a) code for obtaining sequence information for said fetal and maternalnucleic acids in the sample;

(b) code for using said sequence information to computationally identifya number of sequence tags from the fetal and maternal nucleic acids foreach of any one or more chromosomes of interest selected fromchromosomes 1-22, X, and Y and to identify a number of sequence tags forat least one normalizing chromosome sequence or normalizing chromosomesegment sequence for each of said any one or more chromosomes ofinterest;

(c) code for using said number of sequence tags identified for each ofsaid any one or more chromosomes of interest and said number of sequencetags identified for each normalizing chromosome sequence or normalizingchromosome segment sequence to calculate a single chromosome dose foreach of the any one or more chromosomes of interest; and

(d) code for comparing each of the single chromosome doses for each ofthe any one or more chromosomes of interest to a corresponding thresholdvalue for each of the one or more chromosomes of interest, and therebydetermining the presence or absence of any one or more completedifferent fetal chromosomal aneuploidies in the sample.

In some embodiments, the code for calculating a single chromosome dosefor each of the any one or more chromosomes of interest comprises codefor calculating a chromosome dose for a selected one of the chromosomesof interest as the ratio of the number of sequence tags identified forthe selected chromosome of interest and the number of sequence tagsidentified for a corresponding at least one normalizing chromosomesequence or normalizing chromosome segment sequence for the selectedchromosome of interest.

In some embodiments, the system further comprises code for repeating thecalculating of a chromosome dose for each of any remaining chromosomesegments of the any one or more segments of any one or more chromosomesof interest.

In some embodiments, the one or more chromosomes of interest selectedfrom chromosomes 1-22, X, and Y comprise at least twenty chromosomesselected from chromosomes 1-22, X, and Y, and wherein the instructionscomprise instructions for determining the presence or absence of atleast twenty different complete fetal chromosomal aneuploidies isdetermined.

In some embodiments, the at least one normalizing chromosome sequence isa group of chromosomes selected from chromosomes 1-22, X, and Y. Inother embodiments, the at least one normalizing chromosome sequence is asingle chromosome selected from chromosomes 1-22, X, and Y.

In another embodiment, the invention provides a system for use indetermining the presence or absence of any one or more different partialfetal chromosomal aneuploidies in a maternal test sample comprisingfetal and maternal nucleic acids, the system comprising: a sequencer forreceiving a nucleic acid sample and providing fetal and maternal nucleicacid sequence information from the sample; a processor, and a machinereadable storage medium comprising instructions for execution on saidprocessor, the instructions comprising:

(a) code for obtaining sequence information for said fetal and maternalnucleic acids in said sample;

(b) code for using said sequence information to computationally identifya number of sequence tags from the fetal and maternal nucleic acids foreach of any one or more segments of any one or more chromosomes ofinterest selected from chromosomes 1-22, X, and Y and to identify anumber of sequence tags for at least one normalizing segment sequencefor each of said any one or more segments of any one or more chromosomesof interest;

(c) code using said number of sequence tags identified for each of saidany one or more segments of any one or more chromosomes of interest andsaid number of sequence tags identified for said normalizing segmentsequence to calculate a single chromosome segment dose for each of saidany one or more segments of any one or more chromosomes of interest; and

(d) code for comparing each of said single chromosome segment doses foreach of said any one or more segments of any one or more chromosomes ofinterest to a corresponding threshold value for each of said any one ormore chromosome segments of any one or more chromosome of interest, andthereby determining the presence or absence of one or more differentpartial fetal chromosomal aneuploidies in said sample.

In some embodiments, the code for calculating a single chromosomesegment dose comprises code for calculating a chromosome segment dosefor a selected one of the chromosome segments as the ratio of the numberof sequence tags identified for the selected chromosome segment and thenumber of sequence tags identified for a corresponding normalizingsegment sequence for the selected chromosome segment.

In some embodiments, the system further comprises code for repeating thecalculating of a chromosome segment dose for each of any remainingchromosome segments of the any one or more segments of any one or morechromosomes of interest.

In some embodiments, the system further comprises (i) code for repeating(a)-(d) for test samples from different maternal subjects, and (ii) codefor determining the presence or absence of any one or more differentpartial fetal chromosomal aneuploidies in each of said samples.

In other embodiments of any of the systems provided herein, the codefurther comprises code for automatically recording the presence orabsence of a fetal chromosomal aneuploidy as determined in (d) in apatient medical record for a human subject providing the maternal testsample, wherein the recording is performed using the processor.

In some embodiments of any of the systems provided herein, the sequenceris configured to perform next generation sequencing (NGS). In someembodiments, the sequencer is configured to perform massively parallelsequencing using sequencing-by-synthesis with reversible dyeterminators. In other embodiments, the sequencer is configured toperform sequencing-by-ligation. In yet other embodiments, the sequenceris configured to perform single molecule sequencing.

Kits

In various embodiments, kits are provided for practice of the methodsdescribed herein. In certain embodiments the kits comprise one or morepositive internal controls for a full aneuploidy and/or for a partialaneuploidy. Typically, although not necessarily, the controls compriseinternal positive controls comprising nucleic acid sequences of the typethat are to be screened for. For example, a control for a test todetermine the presence or absence of a fetal trisomy e.g. trisomy 21, ina maternal sample can comprises DNA characterized by trisomy 21 (e.g.,DNA obtained from an individual with trisomy 21). In some embodiments,the control comprises a mixture of DNA obtained from two or moreindividuals with different aneuploidies. For example, for a test todetermine the presence or absence of trisomy 13, trisomy 18, trisomy 21,and monosomy X, the control can comprise a combination of DNA samplesobtained from pregnant women each carrying a fetus with one of thetrisomys being tested. In addition to complete chromosomal aneuploidies,IPCs can be created to provide positive controls for tests to determinethe presence or absence of partial aneuploidies.

In certain embodiments the positive control(s) comprise one or morenucleic acids comprising a trisomy 21 (T21), and/or a trisomy 18 (T18),and/or a trisomy 13 (T13). In certain embodiments the nucleic acid(s)comprising each of the trisomys present are T21 are provided in separatecontainers. In certain embodiments the nucleic acids comprising two ormore trisomys are provided in a single container. Thus, for example, incertain embodiments, a container may contain T21 and T18, T21 and T13,T18 and T13. In certain embodiments, a container may contain T18, T21and T13. In these various embodiments, the trisomys may be provided inequal quantity/concentration. In other embodiments, the trisomy may beprovided in particular predetermined ratios. In various embodiments thecontrols can be provided as “stock” solutions of known concentration.

In certain embodiments the control for detecting an aneuploidy comprisesa mixture of cellular genomic DNA obtained from a two subjects one beingthe contributor of the aneuploid genome. For example, as explainedabove, an internal positive control (IPC) that is created as a controlfor a test to determine a fetal trisomy e.g. trisomy 21, can comprise acombination of genomic DNA from a male or female subject carrying thetrisomic chromosome with genomic DNA from a female subject known not tocarry the trisomic chromosome. In certain embodiments the genomic DNA issheared to provide fragments of between about 100-400 bp, between about150-350 bp, or between about 200-300 bp to simulate the circulatingcfDNA fragments in maternal samples.

In certain embodiments the proportion of fragmented DNA from the subjectcarrying the aneuploidy e.g. trisomy 21 in the control, is chosen tosimulate the proportion of circulating fetal cfDNA found in maternalsamples to provide an IPC comprising a mixture of fragmented DNAcomprising about 5%, about 10%, about 15%, about 20%, about 25%, about30%, of DNA from the subject carrying the aneuploidy. In certainembodiments the control comprise DNA from different subjects eachcarrying a different aneuploidy. For example, the IPC can comprise about80% of the unaffected female DNA, and the remaining 20% can be DNA fromthree different subjects each carrying a trisomic chromosome 21, atrisomic chromosome 13, and a trisomic chromosome 18.

In certain embodiments the control(s) comprise cfDNA obtained from amother known to carry a fetus with a known chromosomal aneuploidy. Forexample, the controls can comprise cfDNA obtained from a pregnant womancarrying a fetus with trisomy 21 and/or trisomy 18, and/or trisomy 13.The cfDNA can extracted from the maternal sample, and cloned into abacterial vector and grown in bacteria to provide an ongoing source ofthe IPC. Alternatively, the cloned cfDNA can be amplified by e.g. PCR.

While the controls present in the kits are described above with respectto trisomies, they need not be so limited. It will be appreciated thatthe positive controls present in the kit can be created to reflect otherpartial aneuploidies including for example, various segmentamplification and/or deletions. Thus, for example, where various cancersare known to be associated with particular amplifications or deletionsof substantially complete chromosomal arms the positive control(s) cancomprise a p arm or a q arm of any one or more of chromosomes 1-22, Xand Y. In certain embodiments the control comprises an amplification ofone or more arms selected from the group consisting of 1q, 3q, 4p, 4q,5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q,16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, and/or 22q (see, e.g.,Table 2).

In certain embodiments, the controls comprise aneuploidies for anyregions known to be associated with particular amplifications ordeletions (e.g., breast cancer associated with an amplification at20Q13). Illustrative regions include, but are not limited to 17q23(associated with breast cancer), 19q12 (associate with ovarian cancer),1q21-1q23 (associated with sarcomas and various solid tumors), 8p11-p12(associated with breast cancer), the ErbB2 amplicon, and so forth. Incertain embodiments the controls comprise an amplification or a deletionof a chromosomal region as shown in any one of Tables 3-6. In certainembodiments the controls comprise an amplification or a deletion of achromosomal region comprising a gene as shown in any one of Tables 3-6.In certain embodiments the controls comprise nucleic acid sequencescomprising an amplification of a nucleic acid comprising one or moreoncogenes In certain embodiments the controls comprise nucleic acidsequences comprising an amplification of a nucleic acid comprising oneor more genes selected from the group consisting of MYC, ERBB2 (EFGR),CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET,ERBBJ, CDK4, MYCB, ERBB2, AKT2, MDM2 and CDK4.

The foregoing controls are intended to be illustrative and not limiting.Using the teachings provided herein numerous other controls suitable forincorporation into a kit will be recognized by one of skill in the art.

In various embodiments in addition to the controls or instead of thecontrols, the kits comprise one or more nucleic acids and/or nucleicacid mimics that provide marker sequence(s) suitable for tracking anddetermining sample integrity. In certain embodiments the markerscomprise an antigenomic sequence. In certain embodiments the markersequences range in length from about 30 bp up to about 600 bp in lengthor about 100 bp to about 400 bp in length. In certain embodiments themarker sequence(s) are at least 30 bp (or nt) in length. In certainembodiments the marker is ligated to an adaptor and the length of theadaptor-ligated marker molecule is between about 200 bp (or nt) andabout 600 bp (or nt), between about 250 bp (or nt) and 550 bp (or nt),between about 300 bp (or nt) and 500 bp (or nt), or between about 350and 450. In certain embodiments, the length of the adaptor-ligatedmarker molecule is about 200 bp (or nt). In certain embodiments thelength of a marker molecule can be about 150 bp (or nt), about 160 bp(or nt), 170 bp (or nt), about 180 bp (or nt), about 190 bp (or nt) orabout 200 bp (or nt). In certain embodiments the length of marker rangesup to about 600 bp (or nt).

In certain embodiments the kit provides at least two, or at least three,or at least four, or at least five, or at least six, or at least seven,or at least eight, or at least nine, or at least ten, or at least 11, orat least 12, or at least 13, or at least 14, or at least 15, or at least16, or at least 17m, or at least 18, or at least 19, or at least 20, orat least 25, or at least 30, or at least 35, or at least 40, or at least50 different sequences.

In various embodiments, the markers comprise one or more DNAs or themarkers comprise one or more DNA mimetics. Suitable mimetics include,but are not limited to morpholino derivatives, peptide nucleic acids(PNA), and phosphorothioate DNA. In various embodiments the markers areincorporated into the controls. In certain embodiments the markers areincorporated into adaptor(s) and/or provided ligated to adaptors.

In certain embodiments the kit further includes one or more sequencingadaptors. Such adaptors include, but are not limited to indexedsequencing adaptors. In certain embodiments the adaptors comprise asingle-stranded arm that include an index sequence and one or more PCRpriming sites.

In certain embodiments the kit further comprises a sample collectiondevice for collection of a biological sample. In certain embodiments thesample collection device comprises a device for collecting blood and,optionally a receptacle for containing blood. In certain embodiments thekit comprises a receptacle for containing blood and the receptaclecomprises an anticoagulant and/or cell fixative, and/or one or moreantigenomic marker sequence(s).

In certain embodiments the kit further comprises DNA extraction reagents(e.g., a separation matrix and/or an elution solution). The kits canalso include reagents for sequencing library preparation. Such reagentsinclude, but are not limited to a solution for end-repairing DNA, and/ora solution for dA-tailing DNA, and/or a solution for adaptor ligatingDNA.

In addition, the kits optionally include labeling and/or instructionalmaterials providing directions (e.g., protocols) for the use of thereagents and/or devices provided in the kit. For example, theinstructional materials can teach the use of the reagents to preparesamples and/or to determine copy number variation in a biologicalsample. In certain embodiments the instructional materials teach the useof the materials to detect a trisomy. In certain embodiments theinstructional materials teach the use of the materials to detect acancer or a predisposition to a cancer.

While the instructional materials in the various kits typically comprisewritten or printed materials they are not limited to such. Any mediumcapable of storing such instructions and communicating them to an enduser is contemplated herein. Such media include, but are not limited toelectronic storage media (e.g., magnetic discs, tapes, cartridges,chips), optical media (e.g., CD ROM), and the like. Such media mayinclude addresses to internet sites that provide such instructionalmaterials.

The various method, apparatus, systems and uses are described in furtherdetail in the following Examples which are not in any way intended tolimit the scope of the invention as claimed. The attached Figures aremeant to be considered as integral parts of the specification anddescription of the invention. The following examples are offered toillustrate, but not to limit the claimed invention.

EXPERIMENTAL Example 1

Sample Processing and cfDNA Extraction

Peripheral blood samples were collected from pregnant women in theirfirst or second trimester of pregnancy and who were deemed at risk forfetal aneuploidy. Informed consent was obtained from each participantprior to the blood draw. Blood was collected before amniocentesis orchorionic villus sampling. Karyotype analysis was performed using thechorionic villus or amniocentesis samples to confirm fetal karyotype.

Peripheral blood drawn from each subject was collected in ACD tubes. Onetube of blood sample (approximately 6-9 mlutube) was transferred intoone 15-mL low speed centrifuge tube. Blood was centrifuged at 2640 rpm,4° C. for 10 min using Beckman Allegra 6 R centrifuge and rotor model GA3.8.

For cell-free plasma extraction, the upper plasma layer was transferredto a 15-ml high speed centrifuge tube and centrifuged at 16000×g, 4° C.for 10 min using Beckman Coulter Avanti J-E centrifuge, and JA-14 rotor.The two centrifugation steps were performed within 72 h after bloodcollection. Cell-free plasma comprising cfDNA was stored at −80° C. andthawed only once before amplification of plasma cfDNA or forpurification of cfDNA.

Purified cell-free DNA (cfDNA) was extracted from cell-free plasma usingthe QIAamp Blood DNA Mini kit (Qiagen) essentially according to themanufacturer's instruction. One milliliter of buffer AL and 100 μl ofProtease solution were added to 1 ml of plasma. The mixture wasincubated for 15 minutes at 56° C. One milliliter of 100% ethanol wasadded to the plasma digest. The resulting mixture was transferred toQIAamp mini columns that were assembled with VacValves and VacConnectorsprovided in the QIAvac 24 Plus column assembly (Qiagen). Vacuum wasapplied to the samples, and the cfDNA retained on the column filters waswashed under vacuum with 750 μl of buffer AW1, followed by a second washwith 750 μl of buffer AW24. The column was centrifuged at 14,000 RPM for5 minutes to remove any residual buffer from the filter. The cfDNA waseluted with buffer AE by centrifugation at 14,000 RPM, and theconcentration determined using Qubit™ Quantitation Platform(Invitrogen).

Example 2

Preparation and Sequencing of Primary and Enriched Sequencing Libraries

a. Preparation of Sequencing Libraries—Abbreviated Protocol (ABB)

All sequencing libraries i.e. primary and enriched libraries, wereprepared from approximately 2 ng of purified cfDNA that was extractedfrom maternal plasma. Library preparation was performed using reagentsof the NEBNext™ DNA Sample Prep DNA Reagent Set 1 (Part No. E6000L; NewEngland Biolabs, Ipswich, Mass.), for Illumina® as follows. Becausecell-free plasma DNA is fragmented in nature, no further fragmentationby nebulization or sonication was done on the plasma DNA samples. Theoverhangs of approximately 2 ng purified cfDNA fragments contained in 40μl were converted into phosphorylated blunt ends according to theNEBNext® End Repair Module by incubating in a 1.5 ml microfuge tube thecfDNA with 5 μl 10× phosphorylation buffer, 2 μl deoxynucleotidesolution mix (10 mM each dNTP), 1 μl of a 1:5 dilution of DNA PolymeraseI, 1 μl T4 DNA Polymerase and 1 μl T4 Polynucleotide Kinase provided inthe NEBNext™ DNA Sample Prep DNA Reagent Set 1 for 15 minutes at 20° C.The enzymes were then heat inactivated by incubating the reactionmixture at 75° C. for 5 minutes. The mixture was cooled to 4° C., and dAtailing of the blunt-ended DNA was accomplished using 10 μl of thedA-tailing master mix containing the Klenow fragment (3′ to 5′ exominus) (NEBNext™ DNA Sample Prep DNA Reagent Set 1), and incubating for15 minutes at 37° C. Subsequently, the Klenow fragment was heatinactivated by incubating the reaction mixture at 75° C. for 5 minutes.Following the inactivation of the Klenow fragment, 1 μl of a 1:5dilution of Illumina Genomic Adaptor Oligo Mix (Part No. 1000521;Illumina Inc., Hayward, Calif.) was used to ligate the Illumina adaptors(Non-Index Y-Adaptors) to the dA-tailed DNA using 4 μl of the T4 DNAligase provided in the NEBNext™ DNA Sample Prep DNA Reagent Set 1, byincubating the reaction mixture for 15 minutes at 25° C. The mixture wascooled to 4° C., and the adaptor-ligated cfDNA was purified fromunligated adaptors, adaptor dimers, and other reagents using magneticbeads provided in the Agencourt AMPure XP PCR purification system (PartNo. A63881; Beckman Coulter Genomics, Danvers, Mass.). Eighteen cyclesof PCR were performed to selectively enrich adaptor-ligated cfDNA (25μl) using Phusion® High-Fidelity Master Mix (25 μl; Finnzymes, Woburn,Mass.) and Illumina's PCR primers (0.5 μM each) complementary to theadaptors (Part No. 1000537 and 1000537). The adaptor-ligated DNA wassubjected to PCR (98° C. for 30 seconds; 18 cycles of 98° C. for 10seconds, 65° C. for 30 seconds, and 72° C. for 30; final extension at72° C. for 5 minutes, and hold at 4° C.) using Illumina Genomic PCRPrimers (Part Nos. 100537 and 1000538) and the Phusion HF PCR Master Mixprovided in the NEBNext™ DNA Sample Prep DNA Reagent Set 1, according tothe manufacturer's instructions. The amplified product was purifiedusing the Agencourt AMPure XP PCR purification system (AgencourtBioscience Corporation, Beverly, Mass.) according to the manufacturer'sinstructions available atwww.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. Thepurified amplified product was eluted in 40 μl of Qiagen EB Buffer, andthe concentration and size distribution of the amplified libraries wasanalyzed using the Agilent DNA 1000 Kit for the 2100 Bioanalyzer(Agilent technologies Inc., Santa Clara, Calif.).

b. Preparation of Sequencing Libraries —Full-Length Protocol

The full-length protocol described here is essentially the standardprotocol provided by Illumina, and only differs from the Illuminaprotocol in the purification of the amplified library. The Illuminaprotocol instructs that the amplified library be purified using gelelectrophoresis, while the protocol described herein uses magnetic beadsfor the same purification step. Approximately 2 ng of purified cfDNAextracted from maternal plasma was used to prepare a primary sequencinglibrary using NEBNext™ DNA Sample Prep DNA Reagent Set 1 (Part No.E6000L; New England Biolabs, Ipswich, Mass.) for Illumina® essentiallyaccording to the manufacturer's instructions. All steps except for thefinal purification of the adaptor-ligated products, which was performedusing Agencourt magnetic beads and reagents instead of the purificationcolumn, were performed according to the protocol accompanying theNEBNext™ Reagents for Sample Preparation for a genomic DNA library thatis sequenced using the Illumina® GAII. The NEBNext™ protocol essentiallyfollows that provided by Illumina, which is available atgrcf.jhml.edu/hts/protocols/11257047_ChIP_Sample_Prep.pdf.

The overhangs of approximately 2 ng purified cfDNA fragments containedin 40 μl were converted into phosphorylated blunt ends according to theNEBNext® End Repair Module by incubating the 40 μl cfDNA with 5 μl 10×phosphorylation buffer, 2 μl deoxynucleotide solution mix (10 mM eachdNTP), 1 μl of a 1:5 dilution of DNA Polymerase I, 1 μl T4 DNAPolymerase and 1 μl T4 Polynucleotide Kinase provided in the NEBNext™DNA Sample Prep DNA Reagent Set 1 in a 200 μl microfuge tube in athermal cycler for 30 minutes at 20° C. The sample was cooled to 4° C.,and purified using a QIAQuick column provided in the QIAQuick PCRPurification Kit (QIAGEN Inc., Valencia, Calif.) as follows. The 50 μlreaction was transferred to 1.5 ml microfuge tube, and 250 μl of QiagenBuffer PB were added. The resulting 300 μl were transferred to aQIAquick column, which was centrifuged at 13,000 RPM for 1 minute in amicrofuge. The column was washed with 750 μl Qiagen Buffer PE, andre-centrifuged. Residual ethanol was removed by an additionalcentrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in 39 μlQiagen Buffer EB by centrifugation. dA tailing of 34 μl of theblunt-ended DNA was accomplished using 16 μl of the dA-tailing mastermix containing the Klenow fragment (3′ to 5′ exo minus) (NEBNext™ DNASample Prep DNA Reagent Set 1), and incubating for 30 minutes at 37° C.according to the manufacturer's NEBNext® dA-Tailing Module. The samplewas cooled to 4° C., and purified using a column provided in theMinElute PCR Purification Kit (QIAGEN Inc., Valencia, Calif.) asfollows. The 50 μl reaction was transferred to 1.5 ml microfuge tube,and 250 μl of Qiagen Buffer PB were added. The 300 μl were transferredto the MinElute column, which was centrifuged at 13,000 RPM for 1 minutein a microfuge. The column was washed with 750 μl Qiagen Buffer PE, andre-centrifuged. Residual ethanol was removed by an additionalcentrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in 15 μlQiagen Buffer EB by centrifugation. Ten microliters of the DNA eluatewere incubated with 1 μl of a 1:5 dilution of the Illumina GenomicAdapter Oligo Mix (Part No. 1000521), 15 μl of 2× Quick LigationReaction Buffer, and 4 μl Quick T4 DNA Ligase, for 15 minutes at 25° C.according to the NEBNext® Quick Ligation Module. The sample was cooledto 4° C., and purified using a MinElute column as follows. One hundredand fifty microliters of Qiagen Buffer PE were added to the 30 μlreaction, and the entire volume was transferred to a MinElute columnwere transferred to a MinElute column, which was centrifuged at 13,000RPM for 1 minute in a microfuge. The column was washed with 750 μlQiagen Buffer PE, and re-centrifuged. Residual ethanol was removed by anadditional centrifugation for 5 minutes at 13,000 RPM. The DNA waseluted in 28 μl Qiagen Buffer EB by centrifugation. Twenty threemicroliters of the adaptor-ligated DNA eluate were subjected to 18cycles of PCR (98° C. for 30 seconds; 18 cycles of 98° C. for 10seconds, 65° C. for 30 seconds, and 72° C. for 30; final extension at72° C. for 5 minutes, and hold at 4° C.) using Illumina Genomic PCRPrimers (Part Nos. 100537 and 1000538) and the Phusion HF PCR Master Mixprovided in the NEBNext™ DNA Sample Prep DNA Reagent Set 1, according tothe manufacturer's instructions. The amplified product was purifiedusing the Agencourt AMPure XP PCR purification system (AgencourtBioscience Corporation, Beverly, Mass.) according to the manufacturer'sinstructions available atwww.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. TheAgencourt AMPure XP PCR purification system removes unincorporateddNTPs, primers, primer dimers, salts and other contaminates, andrecovers amplicons greater than 100 bp. The purified amplified productwas eluted from the Agencourt beads in 40 μl of Qiagen EB Buffer and thesize distribution of the libraries was analyzed using the Agilent DNA1000 Kit for the 2100 Bioanalyzer (Agilent technologies Inc., SantaClara, Calif.).

c. Analysis of Sequencing Libraries Prepared According to theAbbreviated (a) and the Full-Length (b) Protocols

The electropherograms generated by the Bioanalyzer are shown in FIGS.21A and 21B. FIG. 21A shows the electropherogram of library DNA preparedfrom cfDNA purified from plasma sample M24228 using the full-lengthprotocol described in (a), and FIG. 21B shows the electropherogram oflibrary DNA prepared from cfDNA purified from plasma sample M24228 usingthe full-length protocol described in (b). In both figures, peaks 1 and4 represent the 15 bp Lower Marker, and the 1,500 Upper Marker,respectively; the numbers above the peaks indicate the migration timesfor the library fragments; and the horizontal lines indicate the setthreshold for integration. The electrophoregram in FIG. 21A shows aminor peak of fragments of 187 bp and a major peak of fragments of 263bp, while the electropherogram in FIG. 21B shows only one peak at 265bp. Integration of the peak areas resulted in a calculated concentrationof 0.40 ng/μl for the DNA of the 187 bp peak in FIG. 21A, aconcentration of 7.34 ng/μl for the DNA of the 263 bp peak in FIG. 21A,and a concentration of 14.72 ng/μl for the DNA of the 265 bp peak inFIG. 21B. The Illumina adaptors that were ligated to the cfDNA are knownto be 92 bp, which when subtracted from the 265 bp, indicate that thepeak size of the cfDNA is 173 bp. It is possible that the minor peak at187 bp represents fragments of two primers that were ligated end-to-end.The linear two-primer fragments are eliminated from the final libraryproduct when the abbreviated protocol is used. The abbreviated protocolalso eliminates other smaller fragments of less than 187 bp. In thisexample, the concentration of purified adaptor-ligated cfDNA is doublethat of the adaptor-ligated cfDNA produced using the full-lengthprotocol. It has been noted that the concentration of theadaptor-ligated cfDNA fragments was always greater than that obtainedusing the full-length protocol (data not shown).

Thus, an advantage of preparing the sequencing library using theabbreviated protocol is that the library obtained consistently comprisesonly one major peak in the 262-267 bp range while the quality of thelibrary prepared using the full-length protocol varies as reflected bythe number and mobility of peaks other than that representing the cfDNA.Non-cfDNA products would occupy space on the flow cell and diminish thequality of the cluster amplification and subsequent imaging of thesequencing reactions, which underlies the overall assignment of theaneuploidy status. The abbreviated protocol was shown not to affect thesequencing of the library.

Another advantage of preparing the sequencing library using theabbreviated protocol is that the three enzymatic steps of blunt-ending,d-A tailing, and adaptor-ligation, take less than an hour to complete tosupport the validation and implementation of a rapid aneuploiddiagnostic service.

Another advantage is that the three enzymatic steps of blunt-ending, d-Atailing, and adaptor ligation, are performed in the same reaction tube,thus avoiding multiple sample transfers that would potentially lead toloss of material, and more importantly to possible sample mix-up andsample contamination.

Example 3

Preparation of Sequencing Libraries from Unrepaired cfDNA: AdaptorLigation in Solution

To determine whether the abbreviated protocol could be further shortenedto further expedite sample analysis, sequencing libraries were made fromunrepaired cfDNA, and sequenced using the Illumina Genome Analyzer II aspreviously described.

cfDNA was prepared from peripheral blood samples as described herein.Blunt-ending and phosphorylation of the 5′-phosphate mandated by thepublished protocol for the Illumina platform were not performed toprovide the unrepaired cfDNA sample.

Omitting DNA repair or DNA repair and phosphorylation was determined notto affect the quality or the yield of the sequencing library (data notshown).

2-Step in Solution Method for Non-Indexed Unrepaired DNA

In a first set of experiments, the unrepaired cfDNA was subjected tosimultaneous dA tailing and adaptor ligation by combining both KlenowExo− and T4-DNA ligase in the same reaction mixture as follows: Thirtymicroliters of cfDNA at a concentration between 20-150 pg/μl weredA-tailed (5 μl of 10× NEB buffer #2, 2 μl of 10 nM dNTP, 1 μl of 10 nMATP, and 1 μl of 5000 U/ml of Klenow Exo−), and ligated to IlluminaY-adapters (1 μl of a 1:15 dilution of a 3 μM stock) using 1 μl of a400,000 U/ml T4-DNA ligase, in a reaction volume of 50 μl. Thenon-indexed Y-adapters were from Illumina. The combined reactions wereincubated at 25° C. for 30 minutes. The enzymes were heat inactivated at75° C. for 5 minutes, and the reaction products were stored at 10° C.

The adaptor-ligated product was purified using SPRI beads (AgencourtAMPure XP PCR purification system, Beckman Coulter Genomics), andsubjected to 18 cycles of PCR. The PCR-amplified library was subjectedto purification using SPRI, and was sequenced using Illumina's GenomeAnalyzer IIx or HiSeq to obtain single-end reads of 36 bp according tothe manufacturer's instructions. A large number of 36 bp reads wereobtained, covering approximately 10% of the genome. Upon completion ofsequencing of the sample, the Illumina “Sequencer ControlSoftware/Real-time Analysis” transferred base call files in binaryformat to a network attached storage device for data analysis. Sequencedata was analyzed by means of software designed to run on a Linux serverthat converts the binary format base calls into human readable textfiles using illumines “BCLConverter”, then calls the Open Source“Bowtie” program to align sequences to the reference human genome thatis derived from the hg18 genome provided by National Center forBiotechnology Information (NCBI36/hg18, available on the world wide webathttp://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).

The software reads the sequence data generated from the above procedurethat uniquely aligned to the genome from Bowtie output (bowtieout.txtfiles). Sequence alignments with up to 2 base mis-matches were allowedand included in alignment counts only if they aligned uniquely to thegenome. Sequence alignments with identical start and end coordinates(duplicates) were excluded. Between about 5 and 25 million 36 bp tagswith 2 or less mismatches were mapped uniquely to the human genome. Allmapped tags were counted and included in the calculation of chromosomedoses in both test and qualifying samples. Regions extending from base 0to base 2×10⁶, base 10×10⁶ to base 13×10⁶, and base 23×10⁶ to the end ofchromosome Y, were specifically excluded from the analysis because tagsderived from either male or female fetuses map to these regions of theY-chromosome.

FIG. 22A shows the average (n=16) of the percent of the total number ofsequence tags that mapped to each human chromosome (% ChrN) when thesequencing library was prepared according to the abbreviated protocol(ABB; ⋄) and when the sequencing library was prepared according to therepair-free 2-STEP method (INSOL; □). These data show that preparing thesequencing library using the repair-free 2-STEP method resulted in agreater percent of tags mapped to chromosomes with lower GC content anda smaller percent of tags that mapped to chromosomes with greater GCcontent, when compared to the percent tags that mapped to thecorresponding chromosomes when using the abbreviated method. FIG. 22Brelates the percent sequence tags as a function of the size of thechromosome, and shows that the repair-free method decreases the bias ofsequencing. The regression coefficient for mapped tags obtained fromsequencing libraries prepared according to the abbreviated protocol(ABB; Δ), and the in solution repair-free protocol (2-STEP; □) wereR²=0.9332, and R²=0.9806, respectively.

TABLE 8 Percent GC content/chromosome Size GC (Mbps) (%) Chr1 247 41.37Chr2 243 39.44 Chr3 199 38.74 Chr4 191 38.60 Chr5 181 39.35 Chr6 17139.94 Chr7 159 39.78 Chr8 146 40.30 Chr9 140 40.17 Chr10 135 40.43 Chr11134 41.37 Chr12 132 40.59 Chr13 114 38.24 Chr14 106 40.85 Chr15 10041.80 Chr16 89 44.64 Chr17 79 45.01 Chr18 76 39.66 Chr19 63 48.21 Chr2062 42.05 Chr21 47 40.68 Chr22 50 47.64 ChrX 155 39.26 ChrY 58 37.74

Comparison of the abbreviated to repair-free 2-STEP method was alsoviewed as a ratio of the percent tags mapped to individual chromosomeswhen using the repair-free method to the percent tags mapped to theindividual chromosomes when using the abbreviated method as a functionof the percent GC content of each chromosome. The percent GC contentrelative to chromosome size was calculated based on publishedinformation of chromosome sequences and binning of GC content(Constantini et al., Genome Res 16:536-541 [2006]) and provided in Table8. The results are given in FIG. 22C, which shows that there was anoticeable decrease in the ratio for chromosomes having a high GCcontent, and an increase in the ratio for chromosomes having a low GCcontent. These data clearly show the normalizing effect that therepair-free method has for overcoming GC bias.

These data show that the repair-free method corrects for some of the GCbias that is known to be associated with sequencing of amplified DNA.

To determine whether the repair-free method affected the proportion offetal versus maternal cfDNA that was sequenced, the percent number oftags that mapped to chromosomes x and Y were determined. FIGS. 23A and23B show bar diagrams providing mean and standard deviation of thepercent of tags mapped to chromosomes X (FIG. 23A; % ChrX) and Y (FIG.23B; % ChrY) obtained from sequencing 10 samples of cfDNA purified fromplasma of 10 pregnant women. FIG. 23A shows that a greater number oftags mapped to the X chromosome when using the repair-free methodrelative to that obtained using the abbreviated method. FIG. 23B showsthat the percent tags that mapped to the Y chromosome when using therepair-free method was not different from that when using theabbreviated method.

These data show that the repair-free method does not introduce any biasfor or against sequencing fetal versus maternal DNA i.e. the proportionof fetal sequences that were sequenced was not altered when using therepair-free method.

Taken together, these data show that the repair-free method does notadversely affect the quality of the sequencing library, nor theinformation obtained from sequencing the library. Excluding the DNArepair step required by published protocols lowers the cost of reagents,and expedites the preparation of the sequencing library.

2-Step in Solution Method for Indexed Unrepaired DNA

In a second set of experiments, the unrepaired cfDNA was subjected to dAtailing, followed by heat-inactivation of the Klenow Exo−, and adaptorligation. Exclusion of the heat-inactivation of the Klenow Exo− did notaffect either the yield or the quality of the sequencing library whennon-indexed Illumina adaptors, (which carry a 21-base single-strandedarm) were used for the ligation.

To determine whether the repair-free method could be applied tomultiplexed sequencing, home-made indexed Y adaptors comprising a 6 baseindex sequence, were used to generate the libraries by including orexcluding heat-inactivation of the Klenow. Unlike non-indexed adapters,indexed-adapters comprise a 43-base single stranded arm which includesthe index sequence and the PCR priming sites.

Twelve different indexed-adapters identical to Illumina TruSeq adapterswere made starting with oligonucleotides obtained from Integrated DNATechnologies (Coralville, Iowa). Oligonucleotide sequences were obtainedfrom published Illumina TruSeq Indexed-adapter sequences.Oligonucleotides were dissolved to obtain a 300 μM final concentrationAnnealing buffer (10 mM Tris, 1 mM EDTA, 50 mM NaCl, pH 7.5). Equimolarmixtures of oligonucleotides, typically 10 μl each at 300 μM, thatcomprise the two arms of any given indexed-adapter were mixed andallowed to anneal (95° C. for 6 minutes, followed by a slow, controlledcooling from 95° C. to 10° C.). The final 150 μM adapter was diluted to7.5 μM in 10 mM Tris, 1 mM EDTA, pH 8 and stored at −20° C. until use.

The data showed that when indexed adaptors were used, the librarypreparation by the 2_STEP method did not work if active Klenow Exo− waspresent in the same reaction with ligase and indexed adapter. However,if Klenow Exo− was first heat-inactivated at 75° C. for 5 minutes priorto adding the ligase plus the indexed-adapter, the 2-STEP method workedwell. It is likely that when indexed adapters and active Klenow Exo− arepresent together, the strand-displacement activity of the Klenow Exo−enzyme results in digestion of the long single-stranded DNA arms of theindexed-adaptors, eliminating the PCR primer sites. Electropherograms ofsequencing libraries made using the same cfDNA and enzymes, without andwith a heat-inactivation step after the Klenow Exo− reaction showed thatincluding a heat-inactivation of the Klenow Exo− prior to adding ligaseand the indexed-adapter in the 2-STEP method made a library with theexpected profile, with the major peak at 290 bp (data not shown).Accordingly, as the repair-free method is applicable to multiplexedsequencing, all experiments using indexed-Y-adapters were amended toinclude the heat-inactivation of the Klenow Exo−.

Example 4

Preparation of Sequencing Libraries from Unrepaired cfDNA: AdaptorLigation on a Solid Surface (SS)

1-Step Solid Surface Method for Non-Indexed DNA

To determine whether the repair-free library process could be simplifiedfurther, the repair-free sequencing library preparation method describedin Example 3 was configured to be performed on a solid surface.Sequencing of the prepared libraries was performed as described inExample 3.

cfDNA was prepared from peripheral blood samples as described inExample 1. Polypropylene tubes were coated with streptavidin, washed anda first set of biotinylated indexed-adaptors were bound to thestreptavidin-coated tubes as follows. Tubes of an 8-well PCR tube strip(USA Scientific, Ocala, Fla.) were coated with 0.5 nmoles ofStreptavidin (Thermo Scientific, Rockford, Ill.) in 50 ul of PBS byincubating the SA overnight at 4° C. The tubes were washed four timeswith 200 μl each 1×TE. 7.5 pmoles, 3.75 pmoles, 1.8 pmoles and 0.9pmoles of Biotinylated-Index1-adapters each in 50 μl TE were added induplicate to the SA-coated tubes, and incubated at room temperature for25 minutes. The unbound adaptors were removed and the tubes were washedfour times with 200p of TE. Biotinylated Index1 adaptors were made asdescribed in Example 3, using Biotinylated Universal AdapterOligonucleotide purchased from IDT.

1-Step SS Method Using cfDNA from Non-Pregnant Subjects

In a second strip of PCR tubes control samples (NTC: no templatecontrol) or 30 μl of approximately 120 μg/μl, i.e. about 32 fmoles ofpurified cfDNA obtained from a non-pregnant woman were incubated at 37°C. for 15 minutes with 5 units Klenow Exo− in NEB Buffer #2 with 20nmoles dNTP and 10 nmoles ATP in 50 μl reaction volume. Subsequently,the Klenow enzyme was deactivated by incubating the reaction mixture for5 min at 75° C. The Klenow-DNA mixture was transferred to thecorresponding tubes containing the SA-bound biotinylated adaptors, andthe cfDNA was ligated to the immobilized adaptors by incubating themixture with 400 units T4-DNA Ligase in 10 μl of 1× T4-DNA Ligase bufferat 25° C. for 15 minutes. Subsequently, 7.5 pmoles of non-biotinylatedIndex1-adapters were ligated to the solid-phase bound cfDNA byincubating it with 200 units of T4-DNA Ligase in 10 μl buffer at 25° C.for 15 minutes. The reaction mixture was removed, and the tubes werewashed 5 times with 200 μl of TE buffer. The adaptor-ligated cfDNA wasamplified by PCR using 50 μl of Phusion PCR mix [New England Biolabs)]containing 1 μM each P5 and P7 primers (IDT) and cycled as follows: [30s @98° C., (10 s@98° C., 10 s@50° C., 10 s@ 60° C., 10 s@72° C.)×18cycles, 5′ @72° C., 10° C. incubation]. The resulting library productwas subjected to a SPRI cleaning [Beckman Coulter Genomics], and thequality of the library assessed from the profile obtained by analysisusing a High Sensitivity Bioanalyzer chip [Agilent Technologies, SantaClara, Calif.]]. The profiles showed that solid-phase sequencing librarypreparation of unrepaired cfDNA provides high-yield and high qualitysequencing libraries (data not shown).

1-Step SS Method Using cfDNA from Pregnant Subjects

The solid-surface (SS) method was tested using cfDNA samples obtainedfrom pregnant women.

The cfDNA was prepared from 8 peripheral blood samples obtained frompregnant women as described in Example 1, and sequencing libraries wereprepared from the purified cfDNA as described above. The libraries weresequenced, and sequence information analyzed.

FIG. 24 shows the ratio of the number of non-excluded sites (NE sites)on the reference genome (hg18) and the total number of tags mapped tothe non-excluded sites for each of 5 samples from which cfDNA wasprepared and used to construct a sequencing library according to theabbreviated protocol (ABB) described in Example 2 (filled bars), the insolution repair-free protocol (2-STEP; empty bars) described in Example18, and the solid surface repair-free protocol (1-STEP; gray bars)described in the present example.

The data shown in FIG. 24 shows that the representation of PCR-amplifiedsequences prepared according to the three protocols is comparable,indicating that the solid surface method does not skew the variety ofsequences that are represented in the library.

FIG. 25A shows that the number of sequence tags uniquely mapped to eachof the chromosomes when obtained from sequencing the library preparedaccording to the repair-free solid surface method is comparable to thatobtained when using the in solution repair-free 2-STEP method describedabove. The data show that both repair-free methods decrease the GC biasof the sequencing data.

FIG. 25B shows the relationship between the number of tags mapped to thesize of the chromosome to which the tags were mapped. The regressioncoefficient for mapped tags obtained from sequencing libraries preparedaccording to the abbreviated protocol (ABB), the in solution repair-freeprotocol (2-STEP), and the solid surface repair-free protocol (1-STEP)were R²=0.9352, R²=0.9802, and R²=0.9807, respectively.

FIG. 25C shows the ratio of percent mapped sequence tags per chromosomeobtained from sequencing libraries prepared according to the repair-free2-STEP protocol and the tags per chromosome obtained sequencinglibraries prepared according to the abbreviated protocol (ABB) as afunction of the percent GC content of each chromosome (⋄), and the ratioof percent mapped sequence tags per chromosome obtained from sequencinglibraries prepared according to the repair-free 1-STEP protocol and thetags per chromosome obtained sequencing libraries prepared according tothe abbreviated protocol (ABB) as a function of the percent GC contentof each chromosome (□). Taken together, the data in FIGS. 25B and 25Cshow that the 1-STEP and 2-STEP methods both show similar GCnormalization effects because both omit the DNA repair step of thelibrary process.

To determine whether the repair-free method affected the proportion offetal versus maternal cfDNA that was sequenced, the percent number oftags that mapped to chromosomes x and Y were determined. FIGS. 26A and26B shows a comparison of means and standard deviations of the percentof tags mapped to chromosomes X (FIG. 26A) and Y (FIG. 26B) obtainedfrom sequencing 5 samples of cfDNA purified from plasma of 5 pregnantwomen from the ABB, 2-STEP and 1-STEP methods. FIG. 26A shows that agreater number of tags mapped to the X chromosome when using therepair-free methods (2-STEP and 1-STEP) relative to that obtained usingthe abbreviated method (filled bar). FIG. 26B shows that the percenttags that mapped to the Y chromosome when using the repair-free 2-STEPand 1-STEP methods was not different from that when using theabbreviated method.

These data show that the repair-free solid surface 1-STEP method doesnot introduce any bias for or against sequencing fetal versus maternalDNA i.e. the proportion of fetal sequences that were sequenced was notaltered when using the repair-free solid surface method.

Taken together the data demonstrate that generating sequencing librarieson a solid surface is an easy and viable option for sequencing samplepreparation.

Example 5

High-Throughput Compatibility of the Repair-Free Solid Surface 1-STEPLibrary Preparation Method

To determine whether the Repair-Free 1-STEP method for preparinglibraries for sequencing by NGS technology, could be applied tohigh-throughput sample processing, 96 libraries of cfDNA from 96peripheral blood samples were prepared in a 96 well PCR plate coatedwith SA-bound indexed adaptors. Sequencing of the prepared libraries wasperformed as described in Example 5.

Coating of a first PCR plate with SA, and ligation of biotinylatedindexed adaptors was performed as described in Example 4. Each column ofwells of the 96-well plate was coated with a biotinylated adaptorcomprising a unique index. Using a second 96-well PCR plate, 37different cfDNAs in 30 μl was subjected to dA tailing in the presence of10 μl each of Klenow Master Mix at 37° C. for 15 minutes followed byinactivation of the Klenow enzyme at 75° C. for 5 minutes. SeveralcfDNAs were used in multiple wells for a total of 94 wells with cfDNA; 2wells were used as no-template controls. The dA-tailed cfDNA mixture wastransferred to the first PCR plate and ligated to the bound biotinylatedadaptors in the presence of 10 μl Quick Ligase Master Mix1 at 25° C. for15 minutes using the PCT-225 Gradient Tetrad Thermal Cycler (BioRad,Hercules, Calif.). 10 μl of Ligation Master Mix2 customized for eachindexed-adapter was added and ligated at 5° C. for 15 minutes. UnboundDNA was removed, and the bound DNA-biotinylated adaptor complexes washedfive times with TE buffer. 50 μl of PCR master mix was added to eachwell, and the adaptor-ligated DNA was amplified and subjected to a SPRIcleaning as described in Example 4. The libraries were diluted andanalyzed using HiSens BA chips.

A correlation between the amount of purified cfDNA used to prepare thesequencing libraries and the resulting amount of library product wasmade for 61 clinical samples prepared using the ABB method (FIG. 27A),and 35 research samples prepared using the repair-free SS 1-STEP method(FIG. 27B). These data show that the correlation is considerably greaterfor libraries prepared using the Repair-Free SS 1-STEP method(R2=0.5826; FIG. 27A) when compared to that obtained for librariesprepared using the abbreviated method described in Example 2 (R2=0.1534;FIG. 27B). Note that the cfDNA samples in this comparison are not thesame, because clinical samples are not available for R&D. However, theseresults indicate that the repair-free SS 1-STEP method has consistentlygreater correlation between cfDNA input and library output than the ABBmethod. The correlation was subsequently compared for the 3 methods i.e.ABB, repair-free 2-STEP, and repair-free SS 1-STEP methods usingserially diluted amounts of the same purified cfDNA for all threemethods. As is shown in FIG. 28, the best correlation was obtained whenlibraries were prepared according to the SS 1-STEP method (R²=0.9457;Δ), followed by the 2-STEP method (R²=0.7666; □), and the ABB methodwhich had a significantly lower correlation (R²=0.0386; 0). These datashow that repair-free methods, whether in solution or on a solidsurface, provide consistent and predictable yields than either methodsthat end-modify [DNA repair and phosphorylation] cfDNA, whetherincluding or excluding purification of the repaired DNA and of the dAtailed product.

The time taken for preparing the libraries according to thesolid-surface method described in this example was several times lessthan that taken when the sequencing libraries were prepared according tothe abbreviated method. For example, 10-14 samples can be preparedmanually in approximately 4 hours using the ABB method, and 96 or 192libraries can be prepared manually in 4 and 5 hours, respectively, whenusing the SS 1-STEP method. In addition, the SS 1-STEP method can beeasily automated to prepare libraries in multiple of 96 for multiplexedsequencing using NGS technologies. Thus, the SS method would be suitablefor commercial automated high-throughput analysis of samples.

Analysis of the DNA libraries showed that solid-phase sequencing librarypreparation of unrepaired cfDNA provides high-yield and high qualitysequencing libraries that can be configured for automated processes tofurther expedite sample analysis requiring massively parallel sequencingusing NGS technologies. The solid surface method is applicable torepaired DNA.

Example 6

Multiplex Sequencing of Libraries Prepared According to the 1-STEP SSMethod

The library samples prepared on a 96-well plate by the SS 1-STEP method(Example 20) were sequenced in a multiplexed manner with six differentindexed samples per lane of the Illumina HySeq sequencer flow cell.Sequencing of the prepared libraries was performed as described inExample 2. The data shown in FIG. 29 compares the efficiency of indexingas evaluated by multiplexed sequencing between the 2-STEP (filled bars)and SS 1-STEP (open bars). These data demonstrate that the efficiency ofindexing is not compromised by preparing libraries on a solid surface.FIGS. 30A and 30B show the percent of the total number of sequence tagsthat mapped to each human chromosome (% ChrN; FIG. 30A) when thesequencing library was prepared according to the 1 step solid surfacemethod; and FIG. 30B (R2=0.9807) shows the percent sequence tags as afunction of the size of the chromosome. FIGS. 30A and 30B show that theGC bias of the SS 1-STEP method is same as that of the 2-STEP method,because both processes use the DNA repair-free sample preparationenzymatics.

FIG. 31 shows the percent sequence tags that mapped to the Y-chromosomerelative to the tags that mapped to the X-chromosome, obtained fromsequencing 42 libraries that were prepared using the SS 1-STEP methodwith indexed adapters, and that were sequenced in a multiplexed mannerusing Illumina's sequencing by synthesis with reversible terminatortechnology. The data clearly differentiate samples obtained frompregnant women carrying male fetuses from those carrying female fetuses.

Example 7

Sample Processing and DNA Extraction

Peripheral blood samples were collected from pregnant women in theirfirst or second trimester of pregnancy and who were deemed at risk forfetal aneuploidy. Informed consent was obtained from each participantprior to the blood draw. Blood was collected before amniocentesis orchorionic villus sampling. Karyotype analysis was performed using thechorionic villus or amniocentesis samples to confirm fetal karyotype.

Peripheral blood drawn from each subject was collected in ACD tubes. Onetube of blood sample (approximately 6-9 mlutube) was transferred intoone 15-mL low speed centrifuge tube. Blood was centrifuged at 2640 rpm,4° C. for 10 min using Beckman Allegra 6 R centrifuge and rotor model GA3.8.

For cell-free plasma extraction, the upper plasma layer was transferredto a 15-ml high speed centrifuge tube and centrifuged at 16000×g, 4° C.for 10 min using Beckman Coulter Avanti J-E centrifuge, and JA-14 rotor.The two centrifugation steps were performed within 72 h after bloodcollection. Cell-free plasma was stored at −80° C. and thawed only oncebefore DNA extraction.

Cell-free DNA was extracted from cell-free plasma by using QIAamp DNABlood Mini kit (Qiagen) according to the manufacturer's instructions.Five milliliters of buffer AL and 500 μl of Qiagen Protease were addedto 4.5 ml-5 ml of cell-free plasma. The volume was adjusted to 10 mlwith phosphate buffered saline (PBS), and the mixture was incubated at56° C. for 12 minutes. Multiple columns were used to separate theprecipitated cfDNA from the solution by centrifugation at 8,000 RPM in aBeckman microcentrifuge. The columns were washed with AW1 and AW2buffers, and the cfDNA was eluted with 55 μl of nuclease-free water.Approximately 3.5-7 ng of cfDNA was extracted from the plasma samples.

All sequencing libraries were prepared from approximately 2 ng ofpurified cfDNA that was extracted from maternal plasma. Librarypreparation was performed using reagents of the NEBNext™ DNA Sample PrepDNA Reagent Set 1 (Part No. E6000L; New England Biolabs, Ipswich,Mass.), for Illumina® as follows. Because cell-free plasma DNA isfragmented in nature, no further fragmentation by nebulization orsonication was done on the plasma DNA samples. The overhangs ofapproximately 2 ng purified cfDNA fragments contained in 40 μl wereconverted into phosphorylated blunt ends according to the NEBNext® EndRepair Module by incubating in a 1.5 ml microfuge tube the cfDNA with 5μl 10× phosphorylation buffer, 2 μl deoxynucleotide solution mix (10 mMeach dNTP), 1 μl of a 1:5 dilution of DNA Polymerase I, 1 μl T4 DNAPolymerase and 1 μl T4 Polynucleotide Kinase provided in the NEBNext™DNA Sample Prep DNA Reagent Set 1 for 15 minutes at 20° C. The enzymeswere then heat inactivated by incubating the reaction mixture at 75° C.for 5 minutes. The mixture was cooled to 4° C., and dA tailing of theblunt-ended DNA was accomplished using 10 μl of the dA-tailing mastermix containing the Klenow fragment (3′ to 5′ exo minus) (NEBNext™ DNASample Prep DNA Reagent Set 1), and incubating for 15 minutes at 37° C.Subsequently, the Klenow fragment was heat inactivated by incubating thereaction mixture at 75° C. for 5 minutes. Following the inactivation ofthe Klenow fragment, 1 μl of a 1:5 dilution of Illumina Genomic AdaptorOligo Mix (Part No. 1000521; Illumina Inc., Hayward, Calif.) was used toligate the Illumina adaptors (Non-Index Y-Adaptors) to the dA-tailed DNAusing 4p1l of the T4 DNA ligase provided in the NEBNext™ DNA Sample PrepDNA Reagent Set 1, by incubating the reaction mixture for 15 minutes at25° C. The mixture was cooled to 4° C., and the adaptor-ligated cfDNAwas purified from unligated adaptors, adaptor dimers, and other reagentsusing magnetic beads provided in the Agencourt AMPure XP PCRpurification system (Part No. A63881; Beckman Coulter Genomics, Danvers,Mass.). Eighteen cycles of PCR were performed to selectively enrichadaptor-ligated cfDNA using Phusion® High-Fidelity Master Mix(Finnzymes, Woburn, Mass.) and Illumina's PCR primers complementary tothe adaptors (Part No. 1000537 and 1000537). The adaptor-ligated DNA wassubjected to PCR (98° C. for 30 seconds; 18 cycles of 98° C. for 10seconds, 65° C. for 30 seconds, and 72° C. for 30 seconds; finalextension at 72° C. for 5 minutes, and hold at 4° C.) using IlluminaGenomic PCR Primers (Part Nos. 100537 and 1000538) and the Phusion HFPCR Master Mix provided in the NEBNext™ DNA Sample Prep DNA Reagent Set1, according to the manufacturer's instructions. The amplified productwas purified using the Agencourt AMPure XP PCR purification system(Agencourt Bioscience Corporation, Beverly, Mass.) according to themanufacturer's instructions available atwww.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. Thepurified amplified product was eluted in 40 μl of Qiagen EB Buffer, andthe concentration and size distribution of the amplified libraries wasanalyzed using the Agilent DNA 1000 Kit for the 2100 Bioanalyzer(Agilent technologies Inc., Santa Clara, Calif.).

The amplified DNA was sequenced using Illumina's Genome Analyzer II toobtain single-end reads of 36 bp. Only about 30 bp of random sequenceinformation are needed to identify a sequence as belonging to a specifichuman chromosome. Longer sequences can uniquely identify more particulartargets. In the present case, a large number of 36 bp reads wereobtained, covering approximately 10% of the genome. Upon completion ofsequencing of the sample, the Illumina “Sequencer Control Software”transferred image and base call files to a Unix server running theIllumina “Genome Analyzer Pipeline” software version 1.51. The Illumina“Gerald” program was run to align sequences to the reference humangenome that is derived from the hg18 genome provided by National Centerfor Biotechnology Information (NCBI36/hg18, available on the world wideweb athttp://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).The sequence data generated from the above procedure that uniquelyaligned to the genome was read from Gerald output (export.txt files) bya program (c2c.pl) running on a computer running the Linnux operatingsystem. Sequence alignments with base mis-matches were allowed andincluded in alignment counts only if they aligned uniquely to thegenome. Sequence alignments with identical start and end coordinates(duplicates) were excluded.

Between about 5 and 15 million 36 bp tags with 2 or less mismatches weremapped uniquely to the human genome. All mapped tags were counted andincluded in the calculation of chromosome doses in both test andqualifying samples. Regions extending from base 0 to base 2×10⁶, base10×10⁶ to base 13×10⁶, and base 23×10⁶ to the end of chromosome Y, werespecifically excluded from the analysis because tags derived from eithermale or female fetuses map to these regions of the Y-chromosome.

It was noted that some variation in the total number of sequence tagsmapped to individual chromosomes across samples sequenced in the samerun (inter-chromosomal variation), but substantially greater variationwas noted to occur among different sequencing runs (inter-sequencing runvariation).

Example 8

Dose and Variance for Chromosomes 13, 18, 21, X, and Y

To examine the extent of inter-chromosomal and inter-sequencingvariation in the number of mapped sequence tags for all chromosomes,plasma cfDNA obtained from peripheral blood of 48 volunteer pregnantsubjects was extracted and sequenced as described in Example 7, andanalyzed as follows.

The total number of sequence tags that were mapped to each chromosome(sequence tag density) was determined. Alternatively, the number ofmapped sequence tags may be normalized to the length of the chromosometo generate a sequence tag density ratio. The normalization tochromosome length is not a required step, and can be performed solely toreduce the number of digits in a number to simplify it for humaninterpretation. Chromosome lengths that can be used to normalize thesequence tags counts can be the lengths provided on the world wide webat genome.ucsc.edu/goldenPath/stats.html#hg18.

The resulting sequence tag density for each chromosome was related tothe sequence tag density of each of the remaining chromosomes to derivea qualified chromosome dose, which was calculated as the ratio of thesequence tag density for the chromosome of interest e.g. chromosome 21,and the sequence tag density of each of the remaining chromosomes i.e.chromosomes 1-20, 22 and X. Table 9 provides an example of thecalculated qualified chromosome dose for chromosomes of interest 13, 18,21, X, and Y, determined in one of the qualified samples. Chromosomesdoses were determined for all chromosomes in all samples, and theaverage doses for chromosomes of interest 13, 18, 21, X and Y in thequalified samples are provided in Tables 10 and 11, and depicted inFIGS. 32-36. FIGS. 32-36 also depict the chromosome doses for the testsamples. The chromosome doses for each of the chromosomes of interest inthe qualified samples provides a measure of the variation in the totalnumber of mapped sequence tags for each chromosome of interest relativeto that of each of the remaining chromosomes. Thus, qualified chromosomedoses can identify the chromosome or a group of chromosomes i.e.normalizing chromosome that has a variation among samples that isclosest to the variation of the chromosome of interest, and that wouldserve as ideal sequences for normalizing values for further statisticalevaluation. FIGS. 37 and 38 depict the calculated average chromosomedoses determined in a population of qualified samples for chromosomes13, 18, and 21, and chromosomes X and Y.

In some instances, the best normalizing chromosome may not have theleast variation, but may have a distribution of qualified doses thatbest distinguishes a test sample or samples from the qualified samplesi.e. the best normalizing chromosome may not have the lowest variation,but may have the greatest differentiability. Thus, differentiabilityaccounts for the variation in chromosome dose and the distribution ofthe doses in the qualified samples.

Tables 10 and 11 provide the coefficient of variation as the measure ofvariability, and student t-test values as a measure of differentiabilityfor chromosomes 18, 21, X and Y, wherein the smaller the T-test value,the greatest the differentiability. The differentiability for chromosome13 was determined as the ratio of difference between the mean chromosomedose in the qualified samples and the dose for chromosome 13 in the onlyT13 test sample, and the standard deviation of mean of the qualifieddose.

The qualified chromosome doses also serve as the basis for determiningthreshold values when identifying aneuploidies in test samples asdescribed in the following.

TABLE 9 Qualified Chromosome Dose for Chromosomes 13, 18, 21, X and Y (n= 1; sample #11342, 46 XY) Chromo- some chr 21 chr 18 chr 13 chr X chrYchr1 0.149901 0.306798 0.341832 0.490969 0.003958 chr2 0.15413 0.3154520.351475 0.504819 0.004069 chr3 0.193331 0.395685 0.44087 0.6332140.005104 chr4 0.233056 0.476988 0.531457 0.763324 0.006153 chr5 0.2192090.448649 0.499882 0.717973 0.005787 chr6 0.228548 0.467763 0.5211790.748561 0.006034 chr7 0.245124 0.501688 0.558978 0.802851 0.006472 chr80.256279 0.524519 0.584416 0.839388 0.006766 chr9 0.309871 0.6342030.706625 1.014915 0.008181 chr10 0.25122 0.514164 0.572879 0.8228170.006633 chr11 0.257168 0.526338 0.586443 0.8423 0.00679 chr12 0.2751920.563227 0.627544 0.901332 0.007265 chr13 0.438522 0.897509 1 1.4362850.011578 chr14 0.405957 0.830858 0.925738 1.329624 0.010718 chr150.406855 0.832697 0.927786 1.332566 0.010742 chr16 0.376148 0.7698490.857762 1.231991 0.009931 chr17 0.383027 0.783928 0.873448 1.2545210.010112 chr18 0.488599 1 1.114194 1.600301 0.0129 chr19 0.5358671.096742 1.221984 1.755118 0.014148 chr20 0.467308 0.956424 1.0656421.530566 0.012338 chr21 1 2.046668 2.280386 3.275285 0.026401 chr220.756263 1.547819 1.724572 2.476977 0.019966 chrX 0.305317 0.6248820.696241 1 0.008061 chrY 37.87675 77.52114 86.37362 124.0572 1

TABLE 10 Qualified Chromosome Dose, Variance and Differentiability forchromosomes 21, 18 and 13 21 (n = 35) 18 (n = 40) Avg Stdev CV T TestAvg Stdev CV T Test chr1 0.15335 0.001997 1.30 3.18E−10 0.31941 0.0083842.62 0.001675 chr2 0.15267 0.001966 1.29 9.87E−07 0.31807 0.001756 0.554.39E−05 chr3 0.18936 0.004233 2.24 1.04E−05 0.39475 0.002406 0.613.39E−05 chr4 0.21998 0.010668 4.85 0.000501 0.45873 0.014292 3.120.001349 chr5 0.21383 0.005058 2.37 1.43E−05 0.44582 0.003288 0.743.09E−05 chr6 0.22435 0.005258 2.34 1.48E−05 0.46761 0.003481 0.742.32E−05 chr7 0.24348 0.002298 0.94 2.05E−07 0.50765 0.004669 0.929.07E−05 chr8 0.25269 0.003497 1.38 1.52E−06 0.52677 0.002046 0.394.89E−05 chr9 0.31276 0.003095 0.99 3.83E−09 0.65165 0.013851 2.130.000559 chr10 0.25618 0.003112 1.21 2.28E−10 0.53354 0.013431 2.520.002137 chr11 0.26075 0.00247 0.95 1.08E−09 0.54324 0.012859 2.370.000998 chr12 0.27563 0.002316 0.84 2.04E−07 0.57445 0.006495 1.130.000125 chr13 0.41828 0.016782 4.01 0.000123 0.87245 0.020942 2.400.000164 chr14 0.40671 0.002994 0.74 7.33E−08 0.84731 0.010864 1.280.000149 chr15 0.41861 0.007686 1.84 1.85E−10 0.87164 0.027373 3.140.003862 chr16 0.39977 0.018882 4.72 7.33E−06 0.83313 0.050781 6.100.075458 chr17 0.41394 0.02313 5.59 0.000248 0.86165 0.060048 6.970.088579 chr18 0.47236 0.016627 3.52  1.3E−07 chr19 0.59435 0.05064 8.520.01494  1.23932 0.12315 9.94 0.231139 chr20 0.49464 0.021839 4.422.16E−06 1.03023 0.058995 5.73 0.061101 chr21 2.03419 0.08841 4.352.81E−05 chr22 0.84824 0.070613 8.32 0.02209  1.76258 0.169864 9.640.181808 chrX 0.27846 0.015546 5.58 0.000213 0.58691 0.026637 4.540.064883

TABLE 11 Qualified Chromosome Dose, Variance and Differentiability forchromosomes 13, X, and Y 13 (n = 47) X (n = 19) Avg Stdev CV Diff AvgStdev CV T Test chr1 0.36536 0.01775 4.86 1.904 0.56717 0.025988 4.580.001013 chr2 0.36400 0.009817 2.70 2.704 0.56753 0.014871 2.62 chr30.45168 0.007809 1.73 3.592 0.70524 0.011932 1.69 chr4 0.52541 0.0052641.00 3.083 0.82491 0.010537 1.28 chr5 0.51010 0.007922 1.55 3.9440.79690 0.012227 1.53 1.29E−11 chr6 0.53516 0.008575 1.60 3.758 0.835940.013719 1.64 2.79E−11 chr7 0.58081 0.017692 3.05 2.445 0.90507 0.0264372.92 7.41E−07 chr8 0.60261 0.015434 2.56 2.917 0.93990 0.022506 2.392.11E−08 chr9 0.74559 0.032065 4.30 2.102 1.15822 0.047092 4.07 0.000228chr10 0.61018 0.029139 4.78 2.060 0.94713 0.042866 4.53 0.000964 chr110.62133 0.028323 4.56 2.081 0.96544 0.041782 4.33 0.000419 chr12 0.657120.021853 3.33 2.380 1.02296 0.032276 3.16 3.95E−06 chr13 1.567710.014258 0.91 2.47E−15 chr14 0.96966 0.034017 3.51 2.233 1.50951 0.050093.32 8.24E−06 chr15 0.99673 0.053512 5.37 1.888 1.54618 0.077547 5.020.002925 chr16 0.95169 0.080007 8.41 1.613 1.46673 0.117073 7.980.114232 chr17 0.98547 0.091918 9.33 1.484 1.51571 0.132775 8.760.188271 chr18 1.13124 0.040032 3.54 2.312 1.74146 0.072447 4.160.001674 chr19 1.41624 0.174476 12.32 1.306 2.16586 0.252888 11.680.460752 chr20 1.17705 0.094807 8.05 1.695 1.81576 0.137494 7.570.08801  chr21 2.33660 0.131317 5.62 1.927 3.63243 0.235392 6.480.00675  chr22 2.01678 0.243883 12.09 1.364 3.08943 0.34981 11.320.409449 chrX 0.66679 0.028788 4.32 1.114 chr2-6 0.46751 0.006762 1.454.066 chr3-6 0.50332 0.005161 1.03 5.260 chr_tot 1.13209 0.038485 3.40 2.7E-05 Y (n = 26) Avg Stdev CV T Test Chr 1-22, X 0.00734 0.00261130.81 1.8E−12

Examples of diagnoses of T21, T13, T18 and a case of Turner syndromeobtained using the normalizing chromosomes, chromosome doses anddifferentiability for each of the chromosomes of interest are describedin Example 9.

Example 9

Diagnosis of Fetal Aneuploidy Using Normalizing Chromosomes

To apply the use of chromosome doses for assessing aneuploidy in abiological test sample, maternal blood test samples were obtained frompregnant volunteers and cfDNA was prepared, sequenced and analyzed asdescribed in Examples 1 and 2.

Trisomy 21

Table 12 provides the calculated dose for chromosome 21 in an exemplarytest sample (#11403). The calculated threshold for the positivediagnosis of T21 aneuploidy was set at >2 standard deviations from themean of the qualified (normal) samples. A diagnosis for T21 was givenbased on the chromosome dose in the test sample being greater than theset threshold. Chromosomes 14 and 15 were used as normalizingchromosomes in separate calculations to show that either a chromosomehaving the lowest variability e.g. chromosome 14, or a chromosome havingthe greatest differentiability e.g. chromosome 15, can be used toidentify the aneuploidy. Thirteen T21 samples were identified using thecalculated chromosome doses, and the aneuploidy samples were confirmedto be T21 by karyotype.

TABLE 12 Chromosome Dose for a T21 aneuploidy (sample #11403, 47 XY +21)Sequence Tag Chromosome Chromosome Density Dose for Chr 21 ThresholdChr21 333,660 0.419672 0.412696 Chr14 795,050 Chr21 333,660 0.4410380.433978 Chr15 756,533

Trisomy 18

Table 13 provides the calculated dose for chromosome 18 in a test sample(#11390). The calculated threshold for the positive diagnosis of T18aneuploidy was set at 2 standard deviations from the mean of thequalified (normal) samples. A diagnosis for T18 was given based on thechromosome dose in the test sample being greater than the set threshold.Chromosome 8 was used as the normalizing chromosome. In this instancechromosome 8 had the lowest variability and the greatestdifferentiability. Eight T18 samples were identified using chromosomedoses, and were confirmed to be T18 by karyotype.

These data show that a normalizing chromosome can have both the lowestvariability and the greatest differentiability.

TABLE 13 Chromosome Dose for a T18 aneuploidy (sample #11390, 47 XY +18)Sequence Tag Chromosome Chromosome Density Dose for Chr 18 ThresholdChr18 602,506 0.585069 0.530867 Chr8 1,029,803

Trisomy 13

Table 14 provides the calculated dose for chromosome 13 in a test sample(#51236). The calculated threshold for the positive diagnosis of T13aneuploidy was set at 2 standard deviations from the mean of thequalified samples. A diagnosis for T13 was given based on the chromosomedose in the test sample being greater than the set threshold. Thechromosome dose for chromosome 13 was calculated using either chromosome5 or the group of chromosomes 3, 4, 5, and 6 as the normalizingchromosome. One T13 sample was identified.

TABLE 14 Chromosome Dose for a T13 aneuploidy (sample #51236, 47 XY +13) Sequence Tag Chromosome Chromosome Density Dose for Chr 13 ThresholdChr13 692,242 0.541343 0.52594 Chr5 1,278,749 Chr13 692,242 Chr3-61,304,954 0.530472 0.513647 [average]

The sequence tag density for chromosomes 3-6 is the average tag countsfor chromosomes 3-6.

The data show that the combination of chromosomes 3, 4, 5 and 6 providea variability that is lower than that of chromosome 5, and the greatestdifferentiability than any of the other chromosomes.

Thus, a group of chromosomes can be used as the normalizing chromosometo determine chromosome doses and identify aneuploidies.

Turner Syndrome (Monosomy X)

Table 15 provides the calculated dose for chromosomes X and Y in a testsample (#51238). The calculated threshold for the positive diagnosis ofTurner Syndrome (monosomy X) was set for the X chromosome at <−2standard deviations from the mean, and for the absence of the Ychromosome at <−2 standard deviations from the mean for qualified(normal) samples.

TABLE 15 Chromosome Dose for a Turners (XO) aneuploidy (sample #51238,45 X) Chromosome Sequence Tag Dose for Chr X Chromosome Density and ChrY Threshold ChrX 873,631 0.786642 0.803832 Chr4 1,110,582 ChrY 1,321Chr_Total 856,623.6 0.001542101 0.00211208 (1-22, X) (Average)

A sample having an X chromosome dose less than that of the set thresholdwas identified as having less than one X chromosome. The same sample wasdetermined to have a Y chromosome dose that was less than the setthreshold, indicating that the sample did not have a Y chromosome. Thus,the combination of chromosome doses for X and Y were used to identifythe Turner Syndrome (monosomy X) samples.

Thus, the method provided enables for the determination of CNV ofchromosomes. In particular, the method enables for the determination ofover- and under-representation chromosomal aneuploidies by massivelyparallel sequencing of maternal plasma cfDNA and identification ofnormalizing chromosomes for the statistical analysis of the sequencingdata. The sensitivity and reliability of the method allow for accuratefirst and second trimester aneuploidy testing.

Example 10

Determination of Partial Aneuploidy

The use of sequence doses was applied for assessing partial aneuploidyin a biological test sample of cfDNA that was prepared from bloodplasma, and sequenced as described in Example 7. The sample wasconfirmed by karyotyping to have been derived from a subject with apartial deletion of chromosome 11.

Analysis of the sequencing data for the partial aneuploidy (partialdeletion of chromosome 11 i.e. q21-q23) was performed as described forthe chromosomal aneuploidies in the previous examples. Mapping of thesequence tags to chromosome 11 in a test sample revealed a noticeableloss of tag counts between base pairs 81000082-103000103 in the q arm ofthe chromosome relative to the tag counts obtained for correspondingsequence on chromosome 11 in the qualified samples (data not shown).Sequence tags mapped to the sequence of interest on chromosome 11(810000082-103000103 bp) in each of the qualified samples, and sequencetags mapped to all 20 megabase segments in the entire genome in thequalified samples i.e. qualified sequence tag densities, were used todetermine qualified sequence doses as ratios of tag densities in allqualified samples. The average sequence dose, standard deviation, andcoefficient of variation were calculated for all 20 megabase segments inthe entire genome, and the 20-megabase sequence having the leastvariability was the identified normalizing sequence on chromosome 5(13000014-33000033 bp) (See Table 16), which was used to calculate thedose for the sequence of interest in the test sample (see Table 17).Table 16 provides the sequence dose for the sequence of interest onchromosome 11 (810000082-103000103 bp) in the test sample that wascalculated as the ratio of sequence tags mapped to the sequence ofinterest and the sequence tags mapped to the identified normalizingsequence. FIG. 40 shows the sequence doses for the sequence of interestin the 7 qualified samples (O) and the sequence dose for thecorresponding sequence in the test sample (⋄). The mean is shown by thesolid line, and the calculated threshold for the positive diagnosis ofpartial aneuploidy that was set 5 standard deviations from the mean isshown by the dashed line. A diagnosis for partial aneuploidy was basedon the sequence dose in the test sample being less than the setthreshold. The test sample was verified by karyotyping to have deletionq21-q23 on chromosome 11.

Therefore, in addition to identifying chromosomal aneuploidies, themethod of the invention can be used to identify partial aneuploidies.

TABLE 16 Qualified Normalizing Sequence, Dose and Variance for SequenceChr11: 81000082-103000103 (qualified samples n = 7) Chr11:81000082-103000103 Avg Stdev CV Chr5: 1.164702 0.004914 0.42 13000014-33000033

TABLE 17 Sequence Dose for Sequence of Interest (81000082- 103000103) onChromosome 11 (test sample 11206) Chromosome Chromosome Sequence SegmentDose for Segment Tag Density Chr 11 (q21-q23) Threshold Chr11: 81000082-27,052 1.0434313 1.1401347 103000103 Chr5: 13000014- 25,926 33000033

Example 11

Demonstration of Detection of Aneuploidy

Sequencing data obtained for the samples described in Examples 2 and 3,and shown in FIGS. 32-36 were further analyzed to illustrate thesensitivity of the method in successfully identifying aneuploidies inmaternal samples. Normalized chromosome doses for chromosomes 21, 18, 13X and Y were analyzed as a distribution relative to the standarddeviation of the mean (Y-axis) and shown in FIGS. 41A-41E. Thenormalizing chromosome used is shown as the denominator (X-axis).

FIG. 41A shows the distribution of chromosome doses relative to thestandard deviation from the mean for chromosome 21 dose in theunaffected samples (∘) and the trisomy 21 samples (T21; Δ) when usingchromosome 14 as the normalizing chromosome for chromosome 21. FIG. 41Bshows the distribution of chromosome doses relative to the standarddeviation from the mean for chromosome 18 dose in the unaffected samples(∘) and the trisomy 18 samples (T18; Δ) when using chromosome 8 as thenormalizing chromosome for chromosome 18. FIG. 41C shows thedistribution of chromosome doses relative to the standard deviation fromthe mean for chromosome 13 dose in the unaffected samples (∘) and thetrisomy 13 samples (T13; Δ), using the average sequence tag density ofthe group of chromosomes 3, 4, 5, and 6 as the normalizing chromosome todetermine the chromosome dose for chromosome 13. FIG. 41D shows thedistribution of chromosome doses relative to the standard deviation fromthe mean for chromosome X dose in the unaffected female samples (∘), theunaffected male samples (Δ), and the monosomy X samples (XO; +) whenusing chromosome 4 as the normalizing chromosome for chromosome X. FIG.41E shows the distribution of chromosome doses relative to the standarddeviation from the mean for chromosome Y dose in the unaffected malesamples (o the unaffected female sample s (Δ), and the monosomy Xsamples (+), when using the average sequence tag density of the group ofchromosomes 1-22 and X as the normalizing chromosome to determine thechromosome dose for chromosome Y.

The data show that trisomy 21, trisomy 18, trisomy 13 were clearlydistinguishable from the unaffected (normal) samples. The monosomy Xsamples were easily identifiable as having chromosome X dose that wereclearly lower than those of unaffected female samples (FIG. 41D), and ashaving chromosome Y doses that were clearly lower than that of theunaffected male samples (FIG. 41E).

Therefore the method provided is sensitive and specific for determiningthe presence or absence of chromosomal aneuploidies in a maternal bloodsample.

Example 12

Determination of Fetal Chromosomal Abnormalities Using MassivelyParallel DNA Sequencing of Cell Free Fetal DNA from Maternal Blood: TestSet 1 Independent of Training Set 1

The study was conducted by qualified site clinical research personnel at13 US clinic locations between April 2009 and July 2010 under a humansubject protocol approved by institutional review boards (IRBs) at eachinstitution. Informed written consent was obtained from each subjectprior to study participation. The protocol was designed to provide bloodsamples and clinical data to support development of noninvasive prenatalgenetic diagnostic methods. Pregnant women, age 18 years or older wereeligible for inclusion. For patients undergoing clinically indicated CVSor amniocentesis blood was collected prior to performance of theprocedure, and results of fetal karyotype was also collected. Peripheralblood samples (two tubes or ˜20 mL total) were drawn from all subjectsin acid citrate dextrose (ACD) tubes (Becton Dickinson). All sampleswere de-identified and assigned an anonymous patient ID number. Bloodsamples were shipped overnight to the laboratory in temperaturecontrolled shipping containers provided for the study. Time elapsedbetween blood draw and sample receipt was recorded as part of the sampleaccessioning.

Site research coordinators entered clinical data relevant to thepatient's current pregnancy and history into study case report forms(CRFs) using the anonymous patient ID number. Cytogenetic analysis offetal karyotype from invasive prenatal procedure samples was performedper local laboratories and the results were also recorded in study CRFs.All data obtained on CRFs were entered into a clinical database thelaboratory. Cell free plasma was obtained from individual blood tubesutilizing at two-step centrifugation process within 24-48 hours ofsample of venipuncture. Plasma from a single blood tube was sufficientfor sequencing analysis. Cell-free DNA was extracted from cell-freeplasma by using QIAamp DNA Blood Mini kit (Qiagen) according to themanufacturer's instructions. Since the cell free DNA fragments are knownto be approximately 170 base pairs (bp) in length (Fan et al., Clin Chem56:1279-1286 [2010]) no fragmentation of the DNA was required prior tosequencing.

For the training set samples, cfDNA was sent to Prognosys Biosciences,Inc. (La Jolla, Calif.) for sequencing library preparation (cfDNA bluntended and ligated to universal adapters) and sequencing using standardmanufacturer protocols with the Illumina Genome Analyzer IIxinstrumentation (http://www.illumina.com/). Single-end reads of 36 basepairs were obtained. Upon completion of the sequencing, all base callfiles were collected and analyzed. For the test set samples, sequencinglibraries were prepared and sequencing carried out on Illumina GenomeAnalyzer IIx instrument. Sequencing library preparation was performed asfollows. The full-length protocol described is essentially the standardprotocol provided by Illumina, and only differs from the Illuminaprotocol in the purification of the amplified library: the Illuminaprotocol instructs that the amplified library be purified using gelelectrophoresis, while the protocol described herein uses magnetic beadsfor the same purification step. Approximately 2 ng of purified cfDNAthat had been extracted from maternal plasma was used to prepare aprimary sequencing library using NEBNext™ DNA Sample Prep DNA ReagentSet 1 (Part No. E6000L; New England Biolabs, Ipswich, Mass.) forIllumina® essentially according to the manufacturer's instructions. Allsteps except for the final purification of the adaptor-ligated products,which was performed using Agencourt magnetic beads and reagents insteadof the purification column, were performed according to the protocolaccompanying the NEBNex™ Reagents for Sample Preparation for a genomicDNA library that is sequenced using the Illumina® GAII. The NEBNext™protocol essentially follows that provided by Illumina, which isavailable at grcf.jhml.edu/hts/protocols/11257047_ChIP_Sample_Prep.pdf.

The overhangs of approximately 2 ng purified cfDNA fragments containedin 40 μl were converted into phosphorylated blunt ends according to theNEBNext® End Repair Module by incubating the 40p cfDNA with 5 μl 10×phosphorylation buffer, 2 μl deoxynucleotide solution mix (10 mM eachdNTP), 1 μl of a 1:5 dilution of DNA Polymerase I, 1 μl T4 DNAPolymerase and 1 μl T4 Polynucleotide Kinase provided in the NEBNext™DNA Sample Prep DNA Reagent Set 1 in a 200 μl microfuge tube in athermal cycler for 30 minutes at 20° C. The sample was cooled to 4° C.,and purified using a QIAQuick column provided in the QIAQuick PCRPurification Kit (QIAGEN Inc., Valencia, Calif.) as follows. The 501preaction was transferred to 1.5 ml microfuge tube, and 250 μl of QiagenBuffer PB were added. The resulting 300 μl were transferred to aQIAquick column, which was centrifuged at 13,000 RPM for 1 minute in amicrofuge. The column was washed with 750 μl Qiagen Buffer PE, andre-centrifuged. Residual ethanol was removed by an additionalcentrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in 391 μlQiagen Buffer EB by centrifugation. dA tailing of 34 μl of theblunt-ended DNA was accomplished using 16 μl of the dA-tailing mastermix containing the Klenow fragment (3′ to 5′ exo minus) (NEBNext™ DNASample Prep DNA Reagent Set 1), and incubating for 30 minutes at 37° C.according to the manufacturer's NEBNext® dA-Tailing Module. The samplewas cooled to 4° C., and purified using a column provided in theMinElute PCR Purification Kit (QIAGEN Inc., Valencia, Calif.) asfollows. The 50 μl reaction was transferred to 1.5 ml microfuge tube,and 250 μl of Qiagen Buffer PB were added. The 300 μl were transferredto the MinElute column, which was centrifuged at 13,000 RPM for 1 minutein a microfuge. The column was washed with 750 μl Qiagen Buffer PE, andre-centrifuged. Residual ethanol was removed by an additionalcentrifugation for 5 minutes at 13,000 RPM. The DNA was eluted in 15 μlQiagen Buffer EB by centrifugation. Ten microliters of the DNA eluatewere incubated with 1 μl of a 1:5 dilution of the Illumina GenomicAdapter Oligo Mix (Part No. 1000521), 15 μl of 2× Quick LigationReaction Buffer, and 4 μl Quick T4 DNA Ligase, for 15 minutes at 25° C.according to the NEBNext® Quick Ligation Module. The sample was cooledto 4° C., and purified using a MinElute column as follows. One hundredand fifty microliters of Qiagen Buffer PE were added to the 30 μlreaction, and the entire volume was transferred to a MinElute columnwere transferred to a MinElute column, which was centrifuged at 13,000RPM for 1 minute in a microfuge. The column was washed with 750 μlQiagen Buffer PE, and re-centrifuged. Residual ethanol was removed by anadditional centrifugation for 5 minutes at 13,000 RPM. The DNA waseluted in 28 μl Qiagen Buffer EB by centrifugation. Twenty threemicroliters of the adaptor-ligated DNA eluate were subjected to 18cycles of PCR (98° C. for 30 seconds; 18 cycles of 98° C. for 10seconds, 65° C. for 30 seconds, and 72° C. for 30; final extension at72° C. for 5 minutes, and hold at 4° C.) using Illumina Genomic PCRPrimers (Part Nos. 100537 and 1000538) and the Phusion HF PCR Master Mixprovided in the NEBNext™ DNA Sample Prep DNA Reagent Set 1, according tothe manufacturer's instructions. The amplified product was purifiedusing the Agencourt AMPure XP PCR purification system (AgencourtBioscience Corporation, Beverly, Mass.) according to the manufacturer'sinstructions available atwww.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. TheAgencourt AMPure XP PCR purification system removes unincorporateddNTPs, primers, primer dimers, salts and other contaminates, andrecovers amplicons greater than 100 bp. The purified amplified productwas eluted from the Agencourt beads in 40 μl of Qiagen EB Buffer and thesize distribution of the libraries was analyzed using the Agilent DNA1000 Kit for the 2100 Bioanalyzer (Agilent technologies Inc., SantaClara, Calif.). For both the training and test sample sets, single-endreads of 36 base pairs were sequenced.

Data Analysis and Sample Classification

Sequence reads 36 bases in length were aligned to the human genomeassembly hg18 obtained from the UCSC database(http://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/). Alignmentswere carried out utilizing the Bowtie short read aligner (version0.12.5) allowing for up to two base mismatches during alignment(Langmead et al., Genome Biol 10:R25 [2009]. Only reads thatunambiguously mapped to a single genomic location were included. Genomicsites where reads mapped were counted and included in the calculation ofchromosome doses (see below). Regions on the Y chromosome where sequencetags from male and female fetuses map without any discrimination wereexcluded from the analysis (specifically, from base 0 to base 2×10⁶;base 10×10⁶ to base 13×10⁶; and base 23×10⁶ to the end of chromosome Y).

Intra-run and inter-run sequencing variation in the chromosomaldistribution of sequence reads can obscure the effects of fetalaneuploidy on the distribution of mapped sequence sites. To correct forsuch variation, a chromosome dose was calculated as the count of mappedsites for a given chromosome of interest is normalized to countsobserved on a predetermined normalizing chromosome sequence. Asdescribed previously, a normalized chromosome sequence can be composedof a single chromosome or a group of chromosomes. The normalizingchromosome sequence was first identified in a subset of samples in thetraining set of samples that were unaffected i.e. qualified sampleshaving diploid karyotypes for chromosomes of interest 21, 18, 13 and X,considering each autosome as a potential denominator in a ratio ofcounts with our chromosomes of interest. Denominator chromosomes i.e.normalizing chromosome sequences were selected that minimized thevariation of the chromosome doses within and between sequencing runs.Each chromosome of interest was determined to have a distinctnormalizing chromosome sequence (denominator) (Table 18). No singlechromosome could be identified as a normalizing chromosome sequence forchromosome 13 as no one chromosome was determined to reduce thevariability in the dose of chromosome 13 across samples i.e. the spreadof the NCV values for chromosome 13 was not reduced sufficiently toallow for a correct identification of a T13 aneuploidy. Chromosomes 2-6were chosen randomly and tested for their ability as a group to mimicthe behavior of chromosome 13. The group of chromosomes 2-6 was found todiminish substantially the variability in the dose for chromosome 13 inthe training samples, and was thus chosen as the normalizing chromosomesequence for chromosome 13. As described above, the variability inchromosome dose for chromosome Y is greater than 30 independently ofwhich single chromosome is used as the normalizing chromosome sequencein determining the chromosome Y dose. The group of chromosomes 2-6 wasfound to diminish substantially the variability in the dose forchromosome Y in the training samples, and was thus chosen as thenormalizing chromosome sequence for chromosome Y.

The chromosome doses for each of the chromosomes of interest in thequalified samples provides a measure of the variation in the totalnumber of mapped sequence tags for each chromosome of interest relativeto that of each of the remaining chromosomes. Thus, qualified chromosomedoses can identify the chromosome or a group of chromosomes i.e.normalizing chromosome sequence that has a variation among samples thatis closest to the variation of the chromosome of interest, and thatwould serve as ideal sequences for normalizing values for furtherstatistical evaluation.

Chromosome doses for all samples in the training set i.e. qualified andaffected, also serve as the basis for determining threshold values whenidentifying aneuploidies in test samples as described in the following.

TABLE 18 Normalizing Chromosome Sequences for Determining ChromosomeDoses Chromosome of Normalizing Chromosome Chromosome Interest -Numerator Sequence - Denominator of Interest (Chr mapped counts) (Chrmapped counts) 21 Chr 21 Chr 9 18 Chr 18 Chr 8 13 Chr 13 Sum(Chr 2-6) XChr X Chr 6 Y Chr Y Sum(Chr 2-6)

For each chromosome of interest in each sample in the test set, anormalizing value was determined and used to determine the presence orabsence of an aneuploidy. The normalizing value was calculated as achromosome dose that can be further computed to provide a normalizedchromosome value (NCV).

Chromosome Doses

For the test set, a chromosome dose was calculated for each chromosomeof interest, 21, 18, 13, X and Y for every sample. As provided in Table18 above, the chromosome dose for chromosome 21 was calculated as aratio of the number of tags in the test sample that mapped to chromosome21 in the test sample, and the number of tags in the test sample thatmapped to chromosome 9; the chromosome dose for chromosome 18 wascalculated as a ratio of the number of tags in the test sample thatmapped to chromosome 18 in the test sample, and the number of tags inthe test sample that mapped to chromosome 8; the chromosome dose forchromosome 13 was calculated as a ratio of the number of tags in thetest sample that mapped to chromosome 13 in the test sample, and thenumber of tags in the test sample that mapped to chromosomes 2-6; thechromosome dose for chromosome X was calculated as a ratio of the numberof tags in the test sample that mapped to chromosome X in the testsample, and the number of tags in the test sample that mapped tochromosome 6; and the chromosome dose for chromosome Y was calculated asa ratio of the number of tags in the test sample that mapped tochromosome Y in the test sample, and the number of tags in the testsample that mapped to chromosomes 2-6.

Normalized Chromosome Values

Using the chromosome dose for each of the chromosomes of interest ineach of the test samples, and the mean of the corresponding chromosomedose determined in the qualified samples of the training set, anormalized chromosome value (NCV) was calculated using the equation:

${NCV}_{ij} = \frac{x_{ij} - {\hat{\mu}}_{j}}{{\hat{\sigma}}_{j}}$

where {circumflex over (μ)}_(j) AND {circumflex over (σ)}_(j) are theestimated training set mean and standard deviation respectively for thej-th chromosome dose, and x_(ij) is the observed j-th chromosome dosefor sample i. When chromosome doses are normally distributed, the NCV isequivalent to a statistical z-score for the doses. No significantdeparture from linearity is observed in a quantile-quantile plot of theNCVs from unaffected samples. In addition, standard tests of normalityfor the NCVs fail to reject the null hypothesis of normality.

For the test set, an NCV was calculated for each chromosome of interest,21, 18, 13, X and Y for every sample. To insure a safe and effectiveclassification scheme, conservative boundaries were chosen foraneuploidy classification. For classification of the autosomes'aneuploidy state, a NCV>4.0 was required to classify the chromosome asaffected (i.e. aneuploid for that chromosome) and a NCV<2.5 to classifya chromosome as unaffected. Samples with autosomes that have an NCVbetween 2.5 and 4.0 were classified as “no call”.

Sex chromosome classification in the test was performed by sequentialapplication of NCVs for both X and Y as follows:

If NCV Y>−2.0 standard deviations from the mean of male samples, thenthe sample was classified as male (XY).

If NCV Y<−2.0 standard deviations from the mean of male samples, and NCVX>−2.0 standard deviations from the mean of female samples, then thesample was classified as female (XX).

If NCV Y<−2.0 standard deviations from the mean of male samples, and NCVX<−3.0 standard deviations from the mean of female samples, then thesample was classified as monosomy X, i.e. Turner syndrome.

If the NCVs did not fit into any of the above criteria, then the samplewas classified as a “no call” for sex.

Results

Study Population Demographics

A total of 1,014 patients were enrolled between April 2009 and July2010. The patient demographics, invasive procedure type and karyotyperesults are summarized in Table 19. The average age of studyparticipants was 35.6 yrs (range 17 to 47 yrs) and gestational ageranged between 6 weeks, 1 day to 38 weeks, 1 day (mean 15 weeks, 4days). The overall incidence of abnormal fetal chromosome karyotypes was6.8% with T21 incidence of 2.5%. Of 946 subjects with singletonpregnancies and karyotype, 906 (96%) showed at least one clinicallyrecognized risk factor for fetal aneuploidy prior to prenatal procedure.Even eliminating those with advanced maternal age as their soleindication, the data demonstrates a very high false positive rate forcurrent screening modalities. Ultrasound findings of increased nuchaltranslucency, cystic hygroma, or other structural congenital abnormalityby ultrasound were most predictive of abnormal karyotype in this cohort.

TABLE 19 Patient Demographics Total Enrolled Training Set Test Set (N =1014) (N = 71) (N = 48) Dates of Enrollment April 2009- April 2009-January 2010- July 2010 December 2009 June 2010 Number enrolled 1014 435  575  Maternal Age, yrs Mean (SD) 35.6 (5.66)  36.4 (6.05)   34.2(8.22)   Min/Max 17/47 20/46 18/46 Not Specified, N 11 3 0 Ethnicity, N(%) Caucasian 636 (62.7) 50 (70.4) 24 (50.0) Hispanic 167 (16.5) 6 (8.5)13 (27.0) Asian 63 (6.2) 6 (8.5)  5 (10.4) Multi, more than one 53 (5.2)6 (8.5) 1 (2.1) African American 41 (4.0) 1 (1.3) 3 (6.3) Other 36 (3.6)2 (2.8) 1 (2.1) Native American  9 (0.9) 0 (0.0) 1 (2.1) Not Specified 9 (0.9) 0 (0.0) 0 (0.0) Gestational Age, wks, days Mean 15 w 4 d 14 w 5d 15 w 3 d Min/Max 6 w 1 d/ 10 w 0 d/ 10 w 4 d/ 38 w 1 d 23 w 1 d 28 w 3d Number of Fetus, N 1 982  67  47  2 30 4 1 3  2 0 0 PrenatalProcedure, N (%) CVS 430 (42.4) 38 (53.5) 28 (58.3) Amniocentesis 571(56.3) 32 (45.1) 20 (41.7) Not specified  3 (0.3) 1 (1.4) 0 (0.0) Notperformed 10 (1.0) 0 (0.0) 0 (0.0) Fetal Karyotype, N (%) 46 XX 453*(43.9)  22* (29.7)  7* (14.6) 46 XY 474* (45.9)  26* (35.1)  14 (29.2)47, +21, both sexes 25* (2.4)  10* (13.5)  13 (27.1) 47, +18, both sexes14 (1.4) 5 (6.8)  8 (16.7) 47, +13, both sexes  4 (0.4) 2 (2.7) 1 (2.1)45, X  8 (0.8) 3 (4.1) 3 (6.3) Complex, other 18* (1.7)  6 (8.1) 2 (4.2)Karyotype not available 36 (3.5) 0 (0.0) 0 (0.0) Prenatal Screening Non-Analyzed Analyzed Risks for Karyotyped sequenced Training TestSingletons, N (%) N = 834 N = 65 N = 47 AMA only (≥35 years) 445 (53.4)27 (41.5) 21 (44.7) Screen positive 149 (17.9) 18 (27.7)  9 (19.1)(trisomy)** Increased NT 35 (4.2) 3 (4.6)  5 (10.6) Cystic Hygroma 12(1.4) 5 (7.7) 4 (8.5) Cardiac Defect 14 (1.7) 0 (0.0) 4 (8.5) OtherCongenital 78 (9.4) 4 (6.2) 3 (6.4) Abnormality Other Maternal Risk 64(7.7) 5 (7.7) 1 (2.1) None specified 37 (4.4) 3 (4.6) 0 (0.0) *Includesresults of fetuses from multiple gestations, **Assessed and reported byclinicians Abbreviations: AMA = Advanced Maternal Age, NT = nuchaltranslucency

The distribution of diverse ethnic backgrounds represented in this studypopulation is also shown in Table 19. Overall, 63% of the patients inthis study were Caucasian, 17% Hispanic, 6% Asian, 5% multi-ethnic, and4% African American. It was noted that the ethnic diversity variedsignificantly from site to site. For example, one site enrolled 60%Hispanic and 26% Caucasian subjects while three clinics all located inthe same state, enrolled no Hispanic subjects. As expected, there wereno discernible differences observed in our results for differentethnicities.

Training Data Set 1

The training set study selected 71 samples from the initial sequentialaccumulation of 435 samples that were collected between April 2009 andDecember 2009. All subjects with affected fetus' (abnormal karyotypes)in this first series of subjects were included for sequencing and arandom selection and number of non-affected subjects with adequatesample and data. Clinical characteristics of the training set patientswere consistent with the overall study demographics as shown in Table19. The gestational age range of the samples in the training set rangedfrom 10 weeks, 0 days to 23 weeks 1 day. Thirty-eight underwent CVS, 32underwent amniocentesis and 1 patient did not have the invasiveprocedure type specified (an unaffected karyotype 46, XY). 70% of thepatients were Caucasian, 8.5% Hispanic, 8.5% Asian, and 8.5%multi-ethnic. Six sequenced samples were removed from this set for thepurposes of training: 4 samples from subjects with twin gestations(further discussed below), 1 sample with T18 that was contaminatedduring preparation, and 1 sample with a fetal karyotype 69, XXX, leaving65 samples for the training set.

The number of unique sequence sites (i.e. tags identified with uniquesites in the genome) varied from 2.2M in the early phases of thetraining set study to 13.7M in the latter phases due to improvements insequencing technology over time. In order to monitor for any potentialshifts in the chromosome doses over this 6-fold range in unique sites,different unaffected samples were run at the beginning and end of thestudy. For the first 15 unaffected samples run, the average number ofunique sites was 3.8M and the average chromosome doses for chromosome 21and chromosome 18 were 0.314 and 0.528, respectively. For the last 15unaffected samples run, the average number of unique sites was 10.7M andthe average chromosome doses for chromosome 21 and chromosome 18 were0.316 and 0.529, respectively. There was no statistical differencebetween the chromosome doses for chromosome 21 and chromosome 18 overthe time of the training set study.

The training set NCVs for chromosomes 21, 18 and 13 are shown in FIG.42. The results shown in FIG. 42 are consistent with an assumption ofnormality in that roughly 99% of the diploid NCVs would fall within +2.5standard deviations of the mean. Of this set of 65 samples, 8 sampleswith clinical karyotypes indicating T21 had NCVs ranging from 6 to 20.Four samples having clinical karyotypes indicative of fetal T18 had NCVsranging from 3.3 to 12, and the two samples having karyotypes indicativeof fetal trisomy 13 (T13) had NCVs of 2.6 and 4. The spread of the NCVsin affected samples is due to their dependence on the percentage offetal cfDNA in the individual samples.

Similar to the autosomes, the means and standard deviations for the sexchromosomes were established in the training set. The sex chromosomethresholds allowed 100% identification of male and female fetuses in thetraining set.

Test Data Set 1

Having established chromosome doses means and standard deviations fromthe training set, a test set of 48 samples was selected from samplescollected between January 2010 and June 2010 from 575 total samples. Oneof the samples from a twin gestation was removed from the final analysisleaving 47 samples in the test set. Personnel preparing samples forsequencing and operating the equipment were blinded to the clinicalkaryotype information. The gestational age range was similar to thatseen in the training set (Table 19). 58% of the invasive procedures wereCVS, higher than that of the overall procedural demographics, but alsosimilar to the training set. 50% of subjects were Caucasian, 27%Hispanic, 10.4% Asian and 6.3% African American.

In the test set, the number of unique sequence tags varied fromapproximately 13M to 26M. For unaffected samples, the chromosome dosesfor chromosome 21 and chromosome 18 were 0.313 and 0.527, respectively.The test set NCVs for chromosome 21, chromosome 18 and chromosome 13 areshown in FIG. 43 and the classifications are given in Table 20.

TABLE 20 Test Set Classification Data Test Set Classification Data T21classification Unaffected Karyotype for T21 T21 No Call Unaffected forT21 34 47, XX or XY + 21 13 T18 classification Unaffected Karyotype forT18 T18 No Call Unaffected for T18 39 47, XX or XY + 18 8 T13classification Unaffected Karyotype for T13 T13 No Call Unaffected forT13 46 47, XX or XY + 13 1 Sex Chromosome Classification Karyotype XY XXMX* No Call 46, XY 24 46, XX 18 1 45, X 2 1 Cplx 1 *MX is monosomy inthe X chromosome with no evidence of Y chromosomeIn the test set, 13/13 subjects having clinical karyotypes thatindicated fetal T21 were correctly identified having NCVs ranging from 5to 14. Eight/eight subjects having karyotypes that indicated fetal T18were correctly identified having NCVs ranging from 8.5 to 22. The singlesample having a karyotype classified as T13 in this test set wasclassified as a no call with an NCV of approximately 3.

For the test data set, all male samples were correctly identifiedincluding a sample with complex karyotype, 46,XY+marker chromosome(unidentifiable by cytogenetics) (Table 11). Nineteen of twenty femalesamples were correctly identified, and one female sample was categorizedas a no call. For three samples in the test set with karyotype of 45,X,two of the three were correctly identified as monosomy X and 1 wasclassified as a no call (Table 20).

Twins

Four of the samples initially selected for the training set and one ofthe samples in the test set were from twin gestations. The thresholdsbeing employed here could be confounded by the differing amount of cfDNAexpected in the setting of a twin gestation. In the training set, thekaryotype from one of the twin samples was monochorionic 47,XY+21. Asecond twin sample was fraternal and amniocentesis was carried out oneach of the fetuses individually. In this twin gestation, one of thefetuses had a karyotype of 47,XY+21 while the other had a normalkaryotype, 46,XX. In both of these cases the cell free classificationbased on the methods discussed above classified the sample as T21. Theother two twin gestations in the training set were classified correctlyas non-affected for T21 (all twins showed diploid karyotype forchromosome 21). For the twin gestation sample in the test set, karyotypewas only established for Twin B (46,XX) and the algorithm correctlyclassified as non-affected for T21.

CONCLUSION

The data show that massively parallel sequencing can be used todetermine a plurality abnormal fetal karyotypes from the blood ofpregnant women. These data demonstrate that 100% correct classificationof samples with trisomy 21 and trisomy 18 can be identified usingindependent test set data. Even in the case of fetuses with abnormal sexchromosome karyotypes, none of the samples were incorrectly classifiedwith the algorithm of the method. Importantly, the algorithm alsoperformed well in determining the presence of T21 in two sets of twinpregnancies having at least one affected fetus, which has never beenshown previously. Furthermore, this study examined a variety ofsequential samples from multiple centers representing not only the rangeof abnormal karyotypes that one is likely to witness in a commercialclinical setting, but showing the significance of accurately classifyingpregnancies non-affected by common trisomies to address the unacceptablyhigh false positive rates that remain in prenatal screening today. Thedata provide valuable insight into the vast capabilities of employingthis method in the future. Analysis of subsets of the unique genomicsites showed increases in the variance consistent Poisson countingstatistics.

The data build on the findings of Fan and Quake who demonstrated thatthe sensitivity of noninvasive prenatal determination of fetalaneuploidy from maternal plasma using massively parallel sequencing isonly limited by the counting statistics (Fan and Quake, PLos One 5,e10439 [2010]). Because sequencing information was collected across theentire genome, this method is capable of determining any aneuploidy orother copy number variation including insertions and deletions. Thekaryotype from one of the samples had a small deletion in chromosome 11between q21 and q23 that was observed as a ˜10% decrease in the relativenumber of tags in a 25 Mb region starting at q21 when the sequencingdata was analyzed in 500 kbase bins. In addition, in the training set,three of the samples had complex sex karyotypes due to mosaicism in thecytogenetic analysis. These karyotypes were: i) 47,XXX[9]/45,X[6], ii)45,X [3]/46, XY[17], and iii) 47,XXX[13]/45,X[7]. Sample ii, whichshowed some XY-containing cells was correctly classified as XY. Samplesi (from CVS procedure) and iii (from amniocentesis), which both showed amixture of XXX and X cells by cytogenetic analysis (consistent withmosaic Turner syndrome), were classified as a no call and monosomy X,respectively.

In testing the algorithm, another interesting data point was observedhaving an NCV between −5 and −6 for chromosome 21 for one sample fromthe test set (FIG. 43). Although this sample was diploid in chromosome21 by cytogenetics, the karyotype showed mosaicism with partialtriploidy for chromosome 9; 47, XX+9 [9]/46, XX [6]. Since chromosome 9is used in the denominator to determine the chromosome dose forchromosome 21 (Table 18), this lowers the overall NCV value. The abilityof the use of normalizing chromosomes to determine fetal trisomy 9 inthis sample is evidenced by the results provided in Example 13 below.

The conclusion of Fan, et al regarding the sensitivity of these methodsis only correct if the algorithms being utilized are able to account forany random or systematic biases introduced by the sequencing method. Ifthe sequencing data is not properly normalized the resulting analysiswill be inferior to the counting statistics. Chiu, et al noted in theirrecent paper that their measurement of chromosomes 18 and 13 using themassively parallel sequencing method was imprecise, and concluded thatmore research was necessary to apply the method to the determination ofT18 and T13 (Chiu et al., BMJ 342:c7401 [2011]). The method utilized inthe Chiu, et al paper simply uses the number of sequence tags on thechromosome of interest, in their case chromosome 21, normalized by thetotal number of tags in the sequencing run. The challenge for thisapproach is that the distribution of tags on each chromosome can varyfrom sequencing run to sequencing run, and thus increases the overallvariation of the aneuploidy determination metric. In order to comparethe results of the Chiu algorithm to the chromosome doses used in thisexample, the test data for chromosomes 21 and 18 was reanalyzed usingthe method recommended by Chiu, et al. as shown in FIG. 44. Overall, acompression in the range of NCV for each of the chromosomes 21 and 18was observed as well as a decrease in the determination rate with 10/13T21 and 5/8 of the T18 samples correctly identified from our test setutilizing an NCV threshold of 4.0 for aneuploidy classification.

Ehrich, et al also focused only on T21 and used the same algorithm asChiu, et al., (Ehrich et al., Am J Obstet Gynecol 204:205 e1-e11[2011]). In addition, after observing a shift in their test set z-scoremetric from the external reference data i.e. training set, theyretrained on the test set to establish the classification boundaries.Although in principle this approach is feasible, in practice it would bechallenging to decide how many samples are required to train and howoften one would need to retrain to ensure that the classificationboundaries are correct. One method of mitigating this issue is toinclude controls in every sequencing run that measure the baseline andcalibrate for quantitative behavior.

The data obtained using the present method show that massively parallelsequencing is capable of determining multiple fetal chromosomalabnormalities from the plasma of pregnant women when the algorithm fornormalizing the chromosome counting data is optimized. The presentmethod for quantification not only minimizes random and systematicvariations between sequencing runs, but also allows for effectiveclassification of aneuploidies across the entire genome, most notablyT21 and T18. Larger sample collections are required to test thealgorithm for T13 determination. To this end, a prospective, blinded,multi-site clinical study to further demonstrate the diagnostic accuracyof the present method is being performed.

Example 13

Determination of the Presence or Absence of at Least 5 DifferentChromosomal Aneuploidies in all Chromosomes of Individual Test Samples

To demonstrate the capability of the method to determine the presence orabsence of any chromosomal aneuploidy in each of a set of maternal testsamples (test set 1; Example 12), systematically determined normalizingchromosome sequences were identified in unaffected samples of thetraining set (training set 1; Example 12), and used to calculatechromosome doses for all chromosomes in each of the test samples.Determination of the presence or absence of any one or more differentcomplete fetal chromosomal aneuploidies in each of the test and trainingset samples was accomplished from sequencing information obtained from asingle sequencing run on each individual sample.

Using the chromosome densities i.e. the number of sequence tagsidentified for each chromosome in each of the samples of the trainingset described in Example 12, a systematically determined normalizingchromosome sequence consisting of a single chromosome or a group ofchromosomes was determined by calculating a single chromosome dose foreach of chromosomes 1-22, X and Y. The systematically determinednormalizing chromosome sequence for each of chromosomes 1-22, X, and Ywas determined by systematically calculating chromosome doses for eachchromosome using every possible combination of chromosomes as thedenominator. For example, for chromosome 21 as the chromosome ofinterest, chromosome doses were calculated as a ratio of (i) the numberof sequence tags obtained for chromosome 21 (chromosome of interest) and(ii) the number of sequence tags obtained for each of the remainingchromosomes, and the sum of the number of tags obtained for all possiblecombinations of the remaining chromosomes (excluding chromosome 21) i.e.1, 2, 3, 4, 5, etc. up to 20, 21, 22, X, and Y; 1+2, 1+3, 1+4, 1+5, etc.up to 1+20, 1+22, 1+X, and 1+Y; 1+2+3, 1+2+4, 1+2+5 etc. up to 1+2+20,1+2+22, 1+2+X, and 1+2+Y; 1+3+4, 1+3+5, 1+3+6 etc. up to 1+3+20, 1+3+22,1+3+X, and 1+3+Y; 1+2+3+4, 1+2+3+5, 1+2+3+6 etc. up to 1+2+3+20,1+2+3+22, 1+2+3+X, and 1+2+3+Y; and so on such that all possiblecombinations of all of chromosomes 1-20, 22, X and Y were used as anormalizing chromosome sequence (denominator) to determine all possiblechromosome doses for each chromosome of interest in each of thequalified (aneuploid) samples in the training set. Chromosome doses weredetermined in the same manner for chromosome 21 in all training samples,and the systematically determined normalizing chromosome sequence forchromosome 21 was determined as the single or group of chromosomesresulting in a dose for chromosome 21 having the smallest variabilityacross all training samples. The same analysis was repeated to determinethe single chromosome or combination of chromosomes that would serve asthe systematically determined normalizing chromosome sequence for eachof the remaining chromosomes including chromosomes 13, 18, X and Y i.e.all possible combinations of chromosomes were used to determine thenormalizing sequence (single chromosome or a group of chromosomes) forall other chromosomes of interest 1-12, 14-17, 19-20, 22, X and Y, inall training samples. Thus, all chromosomes were treated as chromosomesof interest, and a systematically determined normalizing sequence wasdetermined for each of all chromosomes in each of the unaffected samplesin the training set. Table 21 provides the single or the group ofchromosomes that were identified as the systematically determinednormalizing sequence for each of chromosomes of interest 1-22, X, and Y.As highlighted by Table 21, for some chromosomes of interest, thesystematically determined normalizing chromosome sequence was determinedto be a single chromosome (e.g. when chromosome 4 is the chromosome ofinterest), and for other chromosomes of interest, the systematicallydetermined normalizing chromosome sequence was determined to be a groupof chromosomes (e.g. when chromosome 21 is the chromosome of interest).

TABLE 21 Systematically Determined Normalizing Chromosome Sequences forAll Chromosomes Chromosome Systematically Determined of InterestNormalizing Sequence 1 6 + 10 + 14 + 15 + 17 + 20 2 3 + 6 + 8 + 9 + 10 32 + 4 + 5 + 6 + 12 4  5 5 4 + 6 + 8 + 14 6 3 + 4 + 5 + 12 + 14 7 4 + 5 +8 + 14 + 19 + 20 8 2 + 5 + 7 9 3 + 4 + 8 + 10 + 17 + 19 + 20 + 22 10 2 +14 + 15 + 17 + 20 11 5 + 10 + 14 + 20 + 22 12 1 + 2 + 3 + 5 + 6 + 19 134 + 5 14 1 + 3 + 5 + 6 + 10 + 19 15 1 + 14 + 20 16 14 + 17 + 19 + 20 +22 17 15 + 19 + 22 18 2 + 3 + 5 + 7 19 22 20 10 + 16 + 17 + 22 21 4 +14 + 16 + 20 + 22 22 19 X 4 + 8 Y 4 + 6The mean, standard deviation (SD) and coefficient of variance (CV) forthe systematically determined normalizing chromosome sequence determinedfor each of all chromosomes are given in Table 22.

TABLE 22 Mean, Standard Deviation and Coefficient of Variance for allsystematically determined normalizing chromosome sequences Chromosome ofinterest Mean SD CV  1 0.36637 0.00266 0.72%  2 0.31580 0.00068 0.22%  30.21983 0.00055 0.18%  4 0.98191 0.02509 2.56%  5 0.30109 0.00076 0.25% 6 0.21621 0.00059 0.27%  7 0.21214 0.00044 0.21%  8 0.25562 0.000680.27%  9 0.12726 0.00034 0.27% 10 0.24471 0.00098 0.40% 11 0.269070.00098 0.36% 12 0.12358 0.00029 0.23%  13^(a) 0.26023 0.00122 0.47% 140.09286 0.00028 0.30% 15 0.21568 0.00147 0.68% 16 0.25181 0.00134 0.53%17 0.46000 0.00248 0.54%  18^(a) 0.10100 0.00038 0.38% 19 1.437090.02899 2.02% 20 0.19967 0.00123 0.62%  21^(a) 0.07851 0.00053 0.67% 220.69613 0.01391 2.00% X^(b) 0.46865 0.00279 0.68% Y^(b) 0.00028 0.0000414.97% ^(a)Excluding trisomies ^(b)Female fetus

The variance in chromosome doses across all training samples asreflected by the value of the CV, substantiates the use ofsystematically determined normalizing chromosome sequences to provide alarge signal-to-noise ratio and dynamic range, allowing for thedetermination of the aneuploidies to be made with high sensitivity andhigh specificity, as shown in the following.

To demonstrate the sensitivity and specificity of the method, chromosomedoses for all chromosomes of interest 1-22, X and Y were determined ineach of the samples in the training set, and in each of all samples inthe test set described in Example 11 using the correspondingsystematically determined normalizing chromosome sequences provided inTable 21 above.

Using the systematically determined normalizing chromosome sequence foreach of the chromosomes of interest, the presence or absence of anychromosomal aneuploidy was determined in each of the samples in thetraining set, and in each of the test samples i.e. it was determinedwhether each sample contained a complete fetal chromosomal aneuploidy ofchromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, X, and Y. Sequence information i.e. the number ofsequence tags, was obtained for all chromosomes in each of the samplesin the training set, and in each of the test samples, and a singlechromosome dose for each of the chromosomes in each of the training andtest samples was calculated as described above using the number ofsequence tags obtained for the systematically determined normalizingchromosome sequences corresponding to those determined in the trainedset (Table 21). The number of sequence tags obtained in each of thetraining samples for the systematically determined normalizingchromosome sequences was used to determine the chromosome doses for eachchromosome in each of the training samples, and the number of sequencetags obtained in each of the test samples for the systematicallydetermined normalizing chromosome sequence was used to determine thechromosome dose for each chromosome for each of the test samples. Toensure safe and effective classification of aneuploidies, the sameconservative boundaries were chosen as described in Example 12.

Training Set Results

A plot of the chromosome doses for chromosomes 21, 18 and 13 in thetraining set of samples using the systematically determined normalizingchromosome sequence is given in FIG. 45. When using the systematicallydetermined normalizing chromosome sequence i.e. the group of chromosomes4+14+16+20+22, 8 samples with clinical karyotypes indicating T21 hadNCVs between 5.4 and 21.5. When using the systematically determinednormalizing chromosome sequence i.e. the group of chromosomes 2+3+5+7, 4samples with clinical karyotypes indicating T18 had NCVs between 3.3 and15.3. When using the systematically determined normalizing chromosomesequence i.e. the group of chromosomes 4+5, 2 samples with clinicalkaryotypes indicating T13 had NCVs of 8.0 and 12.4. The T21 samples ofthe training set are shown as the last 8 samples of the chromosome 21data (O); the T18 samples of the training set are shown as the last 4samples of the chromosome 18 data (Δ); and the T13 samples of thetraining set are shown as the last 2 samples of the chromosome 13 data(□).

These data show that normalizing chromosome sequences can be used todetermine and correctly classify different complete fetal chromosomalaneuploidies with great confidence. Since all samples with affectedkaryotypes had NCVs greater than 3, there is less than approximately0.1% probability that these samples are part of the unaffecteddistribution.

Similarly to the autosomes, when the systematically determinednormalizing chromosome sequence (i.e. the group of chromosomes 4+8) wasused for chromosome X, and when the systematically determinednormalizing chromosome sequence (i.e. the group of chromosomes 4+6) wasused for chromosome Y, all of the male and female fetuses in thetraining set were correctly identified. In addition, all 5 of themonosomy X samples were identified. FIG. 46A shows a plot of NCVsdetermined for the X chromosome (X-axis) and NCVs determined for the Ychromosome (Y axis) for each of the samples in the training set. All ofthe samples which are monosomy e X by karyotype have NCV values of lessthan −4.83. Those monosomy X samples that have karyotypes consistentwith a 45,X karyotype (full or mosaic) have a Y NCV value close to zeroas expected. Female samples cluster around NCV=0 for both X and Y.

Test Set Results

A plot of the chromosome doses for chromosomes 21, 18 and 13 in the testsamples using the relevant systematically determined normalizingchromosome sequences is given in FIG. 47. When using the systematicallydetermined normalizing chromosome sequence (i.e. the group ofchromosomes 4+14+16+20+22), then 13 of 13 samples with clinicalkaryotypes indicating T21 were correctly identified with NCVs between7.2 and 16.3. When using the systematically determined normalizingchromosome sequence (i.e. the group of chromosomes 2+3+5+7), then all 8samples with clinical karyotypes indicating T18 were identified withNCVs between 12.7 and 30.7. When using the systematically determinednormalizing chromosome sequence (i.e. the group of chromosomes 4+5),then the only one sample with clinical karyotypes indicating T13 wascorrectly identified with an NCV of 8.6. The T21 samples of the test setare shown as the last 13 samples of the chromosome 21 data (O); the T18samples of the test set are shown as the last 8 samples of thechromosome 18 data (Δ); and the T13 sample of the test set is shown asthe last sample of the chromosome 13 data (□).

These data show that systematically determined normalizing chromosomesequences can be used to determine and correctly classify differentcomplete fetal chromosomal aneuploidies with great confidence. Similarto the training set, all samples with affected karyotypes had NCVsgreater than 7, which indicated an infinitesimally small probabilitythat these samples are part of the unaffected distribution (FIG. 47).

Similarly to the autosomes, when the systematically determinednormalizing chromosome sequence (i.e. the group of chromosomes 4+8) wasused for chromosome X, and when the systematically determinednormalizing chromosome sequence (i.e. the group of chromosomes 4+6) wasused for chromosome Y, all of the male and female fetuses in the testset were correctly identified. In addition, all 3 of the monosomy Xsamples were determined. FIG. 46B shows a plot of NCVs determined forthe X chromosome (X-axis) and NCVs determined for the Y chromosome (Yaxis) for each of the samples in the test set.

As previously described, the present method allows for determining thepresence or absence of a complete, or partial, chromosomal aneuploidy ofeach of chromosomes 1-22, X, and Y in each sample. In addition todetermining complete chromosomal aneuploidies T13, T18, T21, andmonosomy X, the method determined the presence of a trisomy ofchromosome 9 in one of the test samples. When using the systematicallydetermined normalizing chromosome sequence (i.e. the group ofchromosomes 3+4+8+10+17+19+20+22), for chromosome of interest 9, asample having an NCV of 14.4 was identified (FIG. 48). This samplecorresponded to the test sample in Example 12 that was suspected ofbeing aneuploid for chromosome 9 following the calculation of anaberrantly low dose for chromosome 21 (for which chromosome 9 was usedas the normalizing chromosome sequence in Example 12).

The data show that 100% of the samples having clinical karyotypesindicating T21, T13 T18, T9 and monosomy X were correctly identified.FIG. 49 shows a plot of the NCVs for each of chromosomes 1-22 in each ofthe 47 test samples. Medians of NCVs were normalized to zero. The datashow that the method of the invention (including the use ofsystematically determined normalizing chromosome sequences) determinedthe presence of all 5 types of chromosomal aneuploidies that werepresent in this test set with 100% sensitivity and 100% specificity, andclearly indicate that the method can identify any complete chromosomalaneuploidy for any one of chromosomes 1-22, X, and Y, in any sample.

Example 14

Determination of the Presence or Absence of a Partial Fetal ChromosomalAneuploidy: Determination of Cat Eye Syndrome

DiGeorge syndrome (22q11.2 deletion syndrome), a disorder caused by adefect in chromosome 22, results in the poor development of several bodysystems. Medical problems commonly associated with DiGeorge syndromeinclude heart defects, poor immune system function, a cleft palate, poorfunction of the parathyroid glands and behavioral disorders. The numberand severity of problems associated with DiGeorge syndrome vary greatly.Almost everyone with DiGeorge syndrome needs treatment from specialistsin a variety of fields.

To determine the presence or absence of a partial deletion of fetalchromosome 22, a blood sample is obtained by venipuncture for themother, and cfDNA is prepared as described in the Examples above. Thepurified cfDNA is ligated to adaptors and subjected to clusteramplification using the Illumina cBot cluster station. Massivelyparallel sequencing is performed using reversible dye terminators togenerate millions of 36 bp reads. The sequence reads are aligned to thehuman hg19 reference genome, and the reads that are uniquely mapped tothe reference genome are counted as tags.

A set of qualified samples all known to be diploid for chromosome 22i.e. chromosome 22 or any portion thereof is known to be present only ina diploid state, are first sequenced and analyzed to obtain a number ofsequence tags for each of 1000 segments of 3 megabases (Mb) (excludingthe region 22q11.2). Given that the human genome comprises approximately3 billion bases (3 Gb), the 1000 segments of 3 Mb each approximatelycomposes the remainder of the genome. Each of the 1000 segments canserve individually or as in a group of segment sequences that are usedto determine the normalizing segment sequence for the segment ofinterest i.e. the 3 Mb region of 22q11.2. The number of sequence tagsmapped to every single 1000 bp segment is used individually to computesegment doses for the 3 Mb region of 22q11.2. In addition, all possiblecombinations of two or more segments are used to determine segment dosesfor the segment of interest in all qualified samples. The single 3 Mbsegment or the combination of two or more 3 Mb segments that result inthe segment dose having the lowest variability across samples is chosenas the normalizing segment sequence.

The number of sequence tags mapped to the segment of interest in each ofthe qualified samples is used to determine a segment dose in each of thequalified samples. The mean and standard deviation of the segment dosesin all qualified samples is calculated, and used to set threshold s towhich segment doses determined in test samples can be compared.Preferably, normalized segment values (NSV) are calculated for allsegments of interest in all qualified samples, and used to set thethreshold values.

Subsequently, the number of tags mapped to the normalizing segmentsequence in the corresponding test sample is used to determine the doseof the segment of interest in the test sample. A normalized segmentvalue (NSV) is calculated for the segment in the test sample asdescribed previously and the NCV of the segment of interest in the testsample is compared to the threshold determined using the qualifiedsamples to determine the presence or absence of a deletion of 22q11.2 inthe test sample.

A test NCV<−3, indicates that a loss in the segment of interest i.e.partial deletion of chromosome 22 (22q11.2) is present in the testsample.

Example 15

Stool DNA Testing for Prediction of Outcome for StageII ColorectalCancer Patients

Around 30% of all stage II colon cancer patients will relapse and die oftheir disease. Stage II colon cancers of patients who had relapse ofdisease showed significantly more losses on chromosomes 4, 5, 15q, 17qand 18q. In particular, stage II colon cancer patients losses on4q22.1-4q35.2 have been shown to be associated with worse outcome.Determination of the presence or absence of these genomic alterationsmay aid in selecting patients for adjuvant therapy (Brosens et al.,Analytical Cellular Pathology/Cellular Oncology 33: 95-104 [2010]).

To determine the presence or absence of one or more chromosomaldeletions in the 4q22.1 to 4q35.2 region in patients with stage IIcolorectal cancer, stool and/or plasma samples are obtained from thepatient(s). Stool DNA is prepared according to the method described byChen et al., J Natl Cancer Inst 97:1124-1132 [2005]); and plasma DNA isprepared according to the method described in the Examples above. DNA issequenced according to an NGS method described herein, and the sequenceinformation for the patient(s) sample(s) is used to calculate segmentdoses for one or more segments spanning the 4q22.1 to 4q35.2 region.Segment doses are determined using normalizing segment sequences thatare determined a priori by in a set of qualified stool and/or plasmasamples, respectively. Segment doses in the test samples (patientsamples) are calculated, and the presence or absence of one or morepartial chromosomal deletions within the 4q22.1 to 4q35.2 region isdetermined by comparing the NSV for each of the segments of interest tothe threshold set from the NSV in the set of qualified samples.

Example 16

Genome Wide Fetal Aneuploidy Detection by Sequencing of Maternal PlasmaDNA: Diagnostic Accuracy in a Prospective, Blinded, Multicenter Study

The method for determining the presence or absence of aneuploidies inmaternal test samples was used in a prospective study, and itsdiagnostic accuracy was shown as described below. The prospective studyfurther demonstrates the efficacy of the method of the invention todetect fetal aneuploidy for multiple chromosomes across the genome. Theblinded study emulates an actual population of pregnant women in whichthe fetal karyotype is unknown, and all samples with any abnormalkaryotypes were selected for sequencing. Determinations of theclassifications made according to the method of the invention werecompared to fetal karyotypes from invasive procedures to determine thediagnostic performance of the method for multiple chromosomalaneuploidies.

Summary of this Example.

Blood samples were collected in a prospective, blinded study from 2,882women undergoing prenatal diagnostic procedures at 60 United Statessites (clinicaltrials.gov NCT01122524).

An independent biostatistician selected all singleton pregnancies withany abnormal karyotype, and a balanced number of randomly selectedpregnancies with euploid karyotypes. Chromosome classifications weremade for each sample according the method of the invention and comparedto fetal karyotype.

Within an analysis cohort of 532 samples, 89/89 trisomy 21 cases,(sensitivity 100% (95% CI 95.9-100)), 35/36 trisomy 18 cases(sensitivity 97.2%, (95% CI 85.5-99.9)), 11/14 trisomy 13 cases(sensitivity 78.6%, (95% CI 49.2-99.9)), 232/233 females (sensitivity99.6%, (95% CI 97.6->99.9)), 184/184 males (sensitivity 100%, 95% CI98.0-100)), and 15/16 monosomy X cases (sensitivity 93.8%, 95% CI69.8-99.8)) were classified. There were no false positives for autosomalaneuploidies in unaffected subjects (100% specificity, (95%CI>98.5-100)). In addition, fetuses with mosaicism for trisomy 21 (3/3),trisomy 18 (1/1), and monosomy X (2/7), three cases of translocationtrisomy, two cases of other autosomal trisomies (20 and 16) and othersex chromosome aneuploidies (XXX, XXY and XYY) were correctlyclassified.

The results further demonstrate the efficacy of the present method todetect fetal aneuploidy for multiple chromosomes across the genome usingmaternal plasma DNA. The high sensitivity and specificity for thedetection of trisomies 21, 18, 13 and monosomy X suggest that thepresent method can be incorporated into existing aneuploidy screeningalgorithms to reduce unnecessary invasive procedures.

Materials and Methods

The MELISSA (MatErnal BLood IS Source to Accurately diagnose fetalaneuploidy) study was conducted as a prospective, multi-centerobservational study with blinded nested case: control analyses. Pregnantwomen, 18 years and older undergoing an invasive prenatal procedure todetermine fetal karyotype were recruited (Clinicaltrials.govNCT01122524). Eligibility criteria included pregnant women between 8weeks, 0 days and 22 weeks, 0 days gestation who met at least one of thefollowing additional criteria: age >38 years, positive screening testresult (serum analytes and/or nuchal translucency (NT) measurement),presence of ultrasound markers associated with increased risk for fetalaneuploidy, or prior aneuploid fetus. Written informed consent wasobtained from all women who agreed to participate.

Enrollment occurred at 60 geographically dispersed medical centers in 25states per protocol approved by institutional review boards (IRB) ateach institution. Two clinical research organizations (CROs) (Quintiles,Durham, N.C. and Emphusion, San Francisco, Calif.) were retained tomaintain study blinding and provide clinical data management, datamonitoring, biostatistics, and data analysis services.

Before any invasive procedure, a peripheral venous blood sample (17 mL)was collected in two acid citrate dextrose (ACD) tubes (BectonDickinson) that were de-identified and labeled with a unique studynumber. Site research personnel entered study number, date, and time ofblood draw into a secure electronic case report form (eCRF). Whole bloodsamples were shipped overnight in temperature-controlled containers fromsites to the laboratory (Verinata Health, Inc., CA). Upon receipt andsample inspection, cell-free plasma was prepared per previouslydescribed methods (see Example 13) and stored frozen at −80° C. in 2 to4 aliquots until time of sequencing. Date and time of sample receipt atthe laboratory were recorded. A sample was determined to be eligible foranalysis if it was received overnight, was cool to touch, and containedat least 7 mL blood. Samples that were eligible at receipt were reportedto the CRO weekly and used for selection on a random sampling list (seebelow and FIG. 50). Clinical data from the woman's current pregnancy andfetal karyotype were entered into the eCRF by site research personneland verified by CRO monitors through source document review.

Sample size determination was based on the precision of the estimatesfor a targeted range of performance characteristics (sensitivity andspecificity) for the index test. Specifically, the number of affected(T21, T18, T13, male, female, or monosomy X) cases and unaffected(non-T21, non-T18, non-T13, not male, not female, or not monosomy X)controls were determined to estimate the sensitivity and specificity,respectively, to within a pre-specified small margin of error based onthe normal approximation (N=(1.96 √p(1−p)/margin of error)², where p=theestimate of the sensitivity or specificity). Assuming a true sensitivityof 95% or greater, a sample size between 73 to 114 cases ensured thatthe precision of the estimate of sensitivity would be such that thelower bound of the 95% confidence interval (CI) would be 90% or greater(margin of error ≤5%). For smaller sample sizes, a larger estimatedmargin of error of the 95% CI for sensitivity was projected (from 6% to13.5%). To estimate the specificity with greater precision a largernumber of unaffected controls (˜4:1 ratio to cases) were planned at thesampling stage. This ensured the precision of the estimate ofspecificity to at least 3%. Accordingly, as the sensitivity and/orspecificity increased, the precision of the confidence interval wouldalso increase.

Based on sample size determination, a random sampling plan was devisedfor the CRO to generate lists of selected samples to sequence (minimumof 110 cases affected by T21, T18, or T13 and 400 non-affected fortrisomy, allowing up to half of these to have karyotypes other than46,XX or 46,XY). Subjects with a singleton pregnancy and an eligibleblood sample were eligible for selection. Subjects with ineligiblesamples, no karyotype recorded, or a multiple gestation were excluded(FIG. 50). Lists were generated on a regular basis throughout the studyand sent to the Verinata Health laboratory.

Each eligible blood sample was analyzed for six independent categories.The categories were aneuploidy status for chromosomes 21, 18 and 13, andgender status for male, female and monosomy X. While still blinded, oneof three classifications (affected, unaffected, or unclassified) weregenerated prospectively for each of the six independent categories foreach plasma DNA sample. Using this scenario, the same sample could beclassified as affected in one analysis (e.g., aneuploidy for chromosome21) and unaffected for another analysis (e.g., euploid for chromosome18).

Conventional metaphase cytogenetic analysis of cells obtained bychorionic villus sampling (CVS) or amniocentesis was used as thereference standard in this study. Fetal karyotyping was performed indiagnostic laboratories routinely used by the participating sites. Ifafter enrollment a patient underwent both CVS and amniocentesis,karyotype results from amniocentesis were used for study analysis.Fluorescence in situ hybridization (FISH) results for targetingchromosomes 21, 18, 13, X, and Y was allowed if a metaphase karyotypewas not available (Table 24). All abnormal karyotype reports (i.e. otherthan 46, XX and 46, XY) were reviewed by a board-certifiedcytogeneticist and classified as affected or unaffected with respect tochromosomes 21, 18, and 13 and gender status for XX, XY and monosomy X.

Pre-specified protocol conventions defined the following abnormalkaryotypes to be assigned a status of ‘censored’ for karyotype by thecytogeneticist: triploidy, tetraploidy, complex karyotypes other thantrisomy (e.g., mosaicism) that involved chromosomes 21, 18, or 13,mosaics with mixed sex chromosomes, sex chromosome aneuploidy orkaryotypes that could not be fully interpreted by the source document(e.g. marker chromosomes of unknown origin). Since the cytogeneticdiagnosis was not known to the sequencing laboratory, allcytogenetically censored samples were independently analyzed andassigned a classification determined using sequencing informationaccording to the method of the invention (Sequencing Classification),but were not included in the statistical analysis. Censored statuspertained only to the relevant one or more of the six analyses (e.g., amosaic T18 would be censored from chromosome 18 analysis, but considered‘unaffected’ for other analyses, such as chromosomes 21, 13, X, and Y)(Table 25). Other abnormal and rare complex karyotypes, which could notbe fully anticipated at the time of protocol design, were not censoredfrom analysis (Table 26).

The data contained in the eCRF and clinical database were restricted toauthorized users only (at the study sites, CROs, and contract clinicalpersonnel). It was not accessible to any employees at Verinata Healthuntil the time of unblinding.

After receiving random sample lists from the CRO, total cell-free DNA (amixture of maternal and fetal) was extracted from thawed selected plasmasamples as described in Example 13. Sequencing libraries were preparedutilizing the Illumina TruSeq kit v2.5. Sequencing was carried out(6-plex—i.e. 6 samples/lane) was performed on an Illumina HiSeq 2000instrument in the Verinata Health laboratory—Single-end reads of 36 basepairs were obtained. The reads were mapped across the genome, and thesequence tags on each chromosome of interest were counted and used toclassify the sample for independent categories as described above.

The clinical protocol required evidence of fetal DNA presence in orderto report a classification result. A classification of male or aneuploidwas considered sufficient evidence of fetal DNA. In addition, eachsample was also tested for the presence of fetal DNA using two allelespecific methods. In the first method, the AmpflSTR Minifiler kit (LifeTechnologies, San Diego, Calif.) was used to interrogate the presence ofa fetal component in the cell free DNA. Electrophoresis of short tandemrepeat (STR) amplicons was carried out on the ABI 3130 Genetic Analyzerfollowing manufacturer's protocols. All nine STR loci in this kit wereanalyzed by comparing the intensity of each peak reported as apercentage of the sum of the intensities of all peaks, and the presenceof minor peaks was used to provide evidence of fetal DNA. In cases inwhich no minor STR could be identified, an aliquot of the sample wasexamined with a single nucleotide polymorphism (SNP) panel of 15 SNPswith average heterozygosity >0.4 selected from the Kidd et al. panel(Kidd et al., Forensic Sci Int 164(1):20-32 [2006]). Allele specificmethods that can be used to detect and/or quantify fetal DNA in maternalsamples are described in U.S. Patent Publications 20120010085,20110224087, and 20110201507, which are herein incorporated byreference.

Normalized chromosome values (NCVs) were determined by calculating allpossible permutations of denominators for all autosomes and sexchromosomes as described in Example 13, however, because the sequencingis this study was carried out on a different instrument than ourprevious work with multiple samples/lane, new normalizing chromosomedenominators had to be determined. The normalizing chromosomedenominators in the current study were determined based on a trainingset of 110 independent (i.e. not from MELISSA eligible samples)unaffected samples (i.e. qualified samples) sequenced prior to analysisof the study samples. The new normalizing chromosomes denominators weredetermined by calculating all possible permutations of denominators forall autosomes and sex chromosomes that minimized the variation for theunaffected training set for all chromosomes across the genome (Table23).

The NCV rules that were applied to provide the autosome classificationof each test sample were those described in Example 12, i.e. forclassification of aneuploidies of autosomes, a NCV>4.0 was required toclassify the chromosome as affected (i.e. aneuploid for that chromosome)and a NCV<2.5 to classify a chromosome as unaffected. Samples withautosomes that have an NCV between 2.5 and 4.0 were named“unclassified”.

Sex chromosome classification in the present test was performed bysequential application of NCVs for both X and Y as follows:

1. If NCV X<−4.0 AND NCV Y<2.5, then the sample was classified asmonosomy X.

2. If NCV X>−2.5 AND NCV X<2.5 AND NCV Y<2.5, then the sample wasclassified as female (XX).

3. If NCV X>4.0 AND NCV Y<2.5, then the sample was classified as XXX.

4. If NCV X>−2.5 AND NCV X<2.5 AND NCV Y>33, then the sample wasclassified as XXY.

5. If NCV X<−4.0 AND NCV Y>4.0, then the sample was classified as male(XY).

6. If condition 5 was met, but NCV Y was approximately 2 times greaterthan expected for the measured NCV X value, then the sample wasclassified as XYY.

7. If the chromosome X and Y NCVs did not fit into any of the abovecriteria, then the sample was classified as unclassified for sex.

Because the laboratory was blinded to the clinical information, thesequencing results were not adjusted for any of the followingdemographic variables: maternal body mass index, smoking status,presence of diabetes, types of conception (spontaneous or assisted),prior pregnancies, prior aneuploidy, or gestational age. Neithermaternal nor paternal samples were utilized for classification, and theclassifications according to the present method did not depend on themeasurement of specific loci or alleles.

The sequencing results were returned to an independent contractbiostatistician prior to unblinding and analysis. Personnel at the studysites, CROs (including the biostatistician generating random samplinglists) and the contract cytogeneticist were blinded to sequencingresults.

TABLE 23 Systematically Determined Normalizing Chromosome Sequences forAll Chromosomes Chromosome Systematically Determined of InterestNormalizing Sequence 1 6 + 10 + 14 + 15 + 17 + 22 2 1 + 3 + 4 + 6 + 8 +9 + 10 3 +5 + 6 + 10 + 12 4  5 5 3 + 4 + 8 + 12 6 2 + 3 + 4 + 14 7 3 +4 + 6 + 8 + 14 + 16 + 19 8 5 + 6 + 10 9 1 + 2 + 5 + 7 + 8 + 11 + 14 +15 + 16 + 17 + 22 10 2 + 9 + 15 + 16 + 20 11 2 + 8 + 9 + 14 + 16 + 19 +20 12 1 + 3 + 5 + 6 + 8 + 15 + 19 13 4 + 6 14 1 + 3 + 4 + 5 + 9 + 11 +15 + 17 15 1 + 10 + 20 16 20 17 15 + 19 + 22 18 5 + 8 19 22 20 15 + 16 +17 + 22 21 4 + 17 + 22 22 19 X 4 + 5 + 8 Y  4

Statistical methods were documented in a detailed statistical analysisplan for the study. Point estimates for sensitivity and specificityalong with exact 95% confidence intervals using the Clopper-Pearsonmethod were computed for each of the six analysis categories. For allstatistical estimation procedures performed, samples with no fetal DNAdetected, ‘censored’ for complex karyotype (per protocol-definedconventions), or ‘unclassified’ by the sequencing test were removed.

Results

Between June 2010 and August 2011, 2,882 pregnant women were enrolled inthe study. The characteristics of the eligible subjects and the selectedcohort are given in Table 24. Subjects that enrolled and provided blood,but were later found during data monitoring to exceed inclusion criteriaand have an actual gestational age at enrollment beyond 22 weeks, 0 dayswere allowed to remain in the study (n=22) Three of these samples werein the selected set. FIG. 50 shows the flow of samples betweenenrollment and analysis. There were 2,625 samples eligible forselection.

TABLE 24 Patient Demographics Eligible Analyzed Affected PatientsPatients Patients (n = 2882) (n = 534) (n = 221) Maternal Age, yrs Mean(SD) 35.8 (5.93)  35.2 (6.40)  34.4 (6.73)  Min/Max 18/49 18/46 18/46Multiparous, N (%) 2348 (81.5)  425 (79.5) 176 (79.6)  Pregnancy byAssisted 247 (8.6)  38 (7.1) 17 (7.7)  Reproductive Techniques, N (%)Race, N (%) White 2078 (72.1)  388 (72.7) 161 (72.9)  African American338 (11.7)  58 (10.9) 28 (12.7) Asian 271 (9.4)  53 (9.9) 18 (8.1) American Indian or Alaska Native 22 (0.8)  5 (0.9) 2 (0.9) Multi-racial173 (6.0)  30 (5.6) 12 (5.4)  BMI(kg/m²) Mean (SD) 26.6 (5.89)  26.2(5.73)  26.2 (5.64)  Min/Max 15/76 17/59 18/56 Current Smoker, N (%) 165(5.7)  29 (5.4) 6 (2.7) Maternal Diabetes Mellitus, N (%) 61 (2.1) 11(2.1) 6 (2.7) Trimester First 832 (28.9) 165 (30.9) 126 (57.0)  Second2050 (71.1)  369 (69.1) 95 (43.0) Gestational Age (GA)*, wks, days Mean15.5 (3.27)  15.1 (3.16)  14.8 (3.18)  Min/Max  8/31 10/23 10/23Karyotype Source, N (%) CVS 1044 (36.8)  228 (42.7) 121 (54.8) Amniocentesis 1783 (62.8)  301 (56.4) 95 (43.0) Products of Conception10 (0.4)  5 (0.9) 5 (2.2) Amniocentesis after CVS, N (%)  7 (0.2)  1(0.2) 0 (0.0) Karyotype by FISH-only, N (%) 105 (3.6)  18 (3.4) 13(5.9)  Number of Fetuses 1 2797 (97.1)   534 (100.0) 221 (100.0) 2 76(2.6)  0 (0.0) 0 (0.0) 3  7 (0.2)  0 (0.0) 0 (0.0) 4  2 (0.2)  0 (0.0) 0(0.0) Prenatal Risk, N (%) AMA only (≥38 years) 1061 (36.8)  152 (28.5)21 (9.5)  Positive screen risk 622 (21.6)  91 (17.0) 14 (6.3) Ultrasound abnormality 477 (6.6)  122 (22.8)  81 (36.7)** Prioraneuploidy pregnancy 82 (2.8) 15 (2.8) 4 (1.8) More than 1 risk 640(22.2) 154 (28.9)  101 (45.7)** Screening Risk Estimated By, N (%) 1749310 125 Nuchal Translucency measure 179 (10.2)  53 (17.1) 36 (28.8)alone First Trimester Combined 677 (38.7) 117 (37.7) 47 (37.6) SecondTrimester Triple or 414 (23.7)  72 (23.3) 16 (12.8) Quadruple FullyIntegrated (1^(st) and 2^(nd) 137 (7.8)  14 (4.5) 3 (2.4) Trimester)Sequential 218 (12.5)  32 (10.3) 15 (12.0) Other 124 (7.1)  22 (7.1) 8(6.4) Abnormal Fetal Ultrasound, N (%) One or more Soft Marker 837(29.0) 242 (45.3)  166 (75.1)** One or more Major Marker 719 (24.9) 212(39.7) 143 (64.7)  IUGR (<10^(th) percentile) 228 (7.9)   79 (15.8) 65(29.4) Amniotic Fluid Volume 26 (0.9) 11 (2.1) 11 (5.0)  Abnormality 24(0.8)  7 (1.3) 4 (1.8) *GA at time of invasive procedure. **Higherpenetrance of ultrasound abnormalitites in fetuses with abnormalkaryotypes Abbreviations: BMI—Body Mass Index, IUGR—Intrauterine growthretardation

Per the random sampling plan, all eligible subjects with an abnormalkaryotype were selected for analysis (FIG. 50B) as well as a set ofsubjects carrying euploid fetuses so that the total sequenced studypopulation resulted in an approximately 4:1 ratio of unaffected toaffected subjects for trisomies 21. From this process, 534 subjects wereselected. Two samples were subsequently removed from analysis due tosample tracking issues in which a full chain of custody between sampletube and data acquisition did not pass quality audit (FIG. 50). Thisresulted in 532 subjects for analysis contributed by 53 of the 60 studysites. The demographics of the selected cohort were similar to theoverall cohort.

Test Performance

FIGS. 51A-51C show the flow diagram for aneuploidy analysis ofchromosomes 21, 18 and 13 and FIGS. 51D-51F show gender analysis flow.Table 27 shows the sensitivity, specificity and confidence interval foreach of the six analyses, and FIGS. 52, 53, and 54, show the graphicaldistribution of samples according to the NCVs following sequencing. Inall 6 categories of analysis, 16 samples (3.0%) were removed due to nofetal DNA detected. After unblinding, there were no distinguishingclinical features for these samples. The number of censored karyotypesfor each category was dependent on the condition being analyzed (fullydetailed in FIG. 52).

Sensitivity and specificity of the method to detect T21 in the analysispopulation (n=493) were 100% (95% CI=95.9, 100.0) and 100% (95% CI=99.1,100.0), respectively (Table 27 and FIG. 51A). This included correctclassification for one complex T21 karyotype, 47, XX,inv(7)(p22q32),+21, and two translocation T21 arising from Robertsoniantranslocations one of which was also mosaic for monosomy X (45, X,+21,der(14;21)q10;q10)[4]/46, XY,+21,der(14;21)q10;q10)[17] and 46,XY,+21,der(21;21)q10;q10).

Sensitivity and specificity to detect T18 in the analysis population(n=496) were 97.2% (85.5, 99.9) and 100% (99.2, 100.0) (Table 27 andFIG. 51B). Although censored (as per protocol) from the primaryanalysis, four samples with mosaic karyotype for T21 and T18 were allcorrectly classified by the method of the invention as ‘affected’ foraneuploidy (Table 25). Because they were correctly detected they areindicated on the left side of FIGS. 51A and 51B. All remaining censoredsamples were correctly classified as unaffected for trisomies 21, 18,and 13 (Table 25). Sensitivity and specificity to detect T13 in theanalysis population were 78.6% (49.2, 99.9) and 100% (99.2, 100.0) (FIG.51C). One T13 case detected arose from a Robertsonian translocation (46,XY,+13,der(13;13)q10;q10). There were seven unclassified samples in thechromosome 21 analysis (1.4%), five in the chromosome 18 analysis(1.0%), and two in the chromosome 13 analysis (0.4%) (FIG. 51A-51C). Inall categories there was an overlap of three samples that had both acensored karyotype (69,XXX) and no fetal DNA detected. One unclassifiedsample in the chromosome 21 analysis was correctly identified as T13 inthe chromosome 13 analysis and one unclassified sample in the chromosome18 analysis was correctly identified as T21 in the chromosome 21analysis.

TABLE 25 Censored Karyotypes Sequencing Sequencing CensoredClassification Classification Karyotype Category Aneuploidy GenderMosaic Trisomy 21 and 18 (n = 4) 47, XY, +21[5]/46, XY[12] 21 Affected(T21) Male 47, XX, +21[4]/46, XX [5] 21 Affected (T21) Unclassified 47,XY, +21[21]/48, XY, +21 + mar[4]* 21, 18, 13, Affected (T21) Male gender47, XX, +18 [42]/46, XX [8] 18 Affected (T18) Female Other ComplexMosaicism (n = 2) 45, XY, −13[5]/46, XY, r(13) 13 Unaffected (21, Male(p11.1q22)[15] 18, 13) 92, XXXX[20]/46, XX[61] 21, 18, 13, Unaffected(21, Unclassified gender 18, 13) Added material of uncertain origin (n =5) 46, XX, add(X)(p22.1) 21, 18, 13, Unaffected (21, Female gender 18,13) 46, XY, add(10)(q26) 21, 18, 13, Unaffected (21, Male gender 18, 13)46, XY, add(15)(p11.2) 21, 18, 13, Unaffected (21, Male gender 18, 13)47, XY, +mar/46, XY 21, 18, 13, Unaffected (21, Male gender 18, 13) 47,XX + mar [12]/46, XX[8] 21, 18, 13, Unaffected (21, Female gender 18,13) Triploidy (n = 10) 69, XXY 21, 18, 13, Unaffected (21, Unclassifiedgender 18, 13) sex 69, XXX (n = 9) 21, 18, 13, Unaffected (21, Female (n= 5) gender 18, 13) (n = 6) Unclassified Unclassified (n = 4) (n = 3)Sex Chromosome Aneuploidy (n = 10) 47, XXX (n = 4) gender Unaffected(21, XXX (n = 3) 18, 13) (n = 4) Monosomy X (n = 1) 47, XXY (n = 3)gender Unaffected (21, XXY (n = 2) 18, 13) (n = 2) UnclassifiedUnclassified (18)** (n = 1)** and Unaffected (21, 13) (n = 1) 47, XYY (n= 3) gender Unaffected (21, XYY (n = 3) 18, 13) (n = 3) Mosaic MonosomyX (n = 7) 45, X/46, XX (n = 3) gender Unaffected (21, Female (n = 2) 18,13) (n = 3) Monosomy X (n = 1) 45, X/47, XXX gender Unaffected (21,Monosomy X 18, 13) 45, X/46, XY (n = 2) gender Unaffected (21, Male (n =2) 18, 13) (n = 2) 45, X, +21, der(14; 21)(q10; q10)[4]/46, genderAffected (T21) and Male XY, +21, der(14; 21)(q10; q10)[17] Unaffected(18, 13) Other Reasons (n = 3) Gender not disclosed in report (n = 2)gender Unaffected (21, Female (n = 2) 18, 13) 46, XY with maternal cellcontamination gender Unaffected (21, Male (n = 1) 18, 13) *Subjectexcluded from all analysis categories due to marker chromosome in onecell line. **Subject with karyotype 48, XXY, +18 was unclassified inchromosome 18 analysis and sex aneuploidy was not detected.

TABLE 26 Abnormal and complex karyotypes that were not censoredSequencing Sequencing Classification Classification Karyotype AneuploidyGender Monosomy X (n = 20) 45, X (n = 15) Unaffected (21, Monosomy X 18,13) 45, X (n = 4) Unaffected (21, Unclassified 18, 13) 45, X (n = 1)Unaffected (21, Female 18, 13) Other Autosomal Trisomy or PartialTrisomy (n = 5) 47, XX, +16 Chromosome 16 Unclassified aneuploidy 47,XX, +20 Chromosome 20 Unclassified aneuploidy Partial trisomy 6q12q16.3and Unaffected (21, Female 6q16.3, no gender 18, 13)* 47, XY, +22Unaffected (21, Male 18, 13) 47, XX, +22 Unclassified (21, Unclassified18, 13) Translocations (n = 7) Balanced (n = 6) Unaffected (21, correctclass 18, 13) (Male or Female) Unbalanced (n = 1) Unaffected (21, Female18, 13) Other Complex Mosaicism Unaffected (21, correct class (n = 4)18, 13) (Male or Female) Other Complex Variants Unaffected (21, correctclass (n = 4) 18, 13) (Male or Female) *An increased normalizedchromosome value (NCV) of 3.6 was noticed from sequencing tags inchromosome 6 after unblinding.

The sex chromosome analysis population for determining performance ofthe method (female, male, or monosomy X) was 433. Our refined algorithmfor classifying the gender status, which allowed for accuratedetermination of sex chromosome aneuploidies, resulted in a highernumber of unclassified results. Sensitivity and specificity fordetecting diploid female state (XX) were 99.6% (95% CI=97.6, >99.9) and99.5% (95% CI=97.2, >99.9), respectively; sensitivity and specificity todetect male (XY) were both 100% (95% CI=98.0, 100.0); and sensitivityand specificity for detecting monosomy X (45,X) were 93.8% (95% CI=69.8,99.8) and 99.8% (95% CI=98.7, >99.9) (FIGS. 33D-f). Although censoredfrom the analysis (as per protocol), the sequencing classifications ofmosaic monosomy X karyotypes were as follows (Table 25): 2/7 classifiedas monosomy X, 3/7 classified with a Y chromosome component classifiedas XY and 2/7 with XX chromosome component classified as female. Twosamples that were classified according to the method of the invention asmonosomy X had karyotypes of 47, XXX and 46, XX. Eight of ten sexchromosome aneuploidies for karyotypes 47, XXX, 47,XXY and 47,XYY werecorrectly classified (Table 25). If the sex chromosome classificationshad been limited to monosomy X, XY and XX, most of the unclassifiedsamples would have been correctly classified as male, but the XXY andXYY sex aneuploidies would not have been identified.

In addition to accurately classifying trisomies 21, 18, 13 and gender,the sequencing results also correctly classified aneuploidy forchromosomes 16 and 20 in two samples (47,XX,+16 and 47,XX,+20) (Table26). Interestingly, one sample with a clinically complex alteration ofthe long arm of chromosome 6 (6q) and two duplications, one of which was37.5 Mb in size, showed an increased NCV from sequencing tags inchromosome 6 (NCV=3.6). In another sample, aneuploidy of chromosome 2was detected according to the method of the invention but not observedin the fetal karyotype at amniocentesis (46,XX). Other complex karyotypevariants shown in Tables 25 and 26 include samples from fetuses withchromosome inversions, deletions, translocations, triploidy and otherabnormalities that were not detected here, but could potentially beclassified at higher sequencing density and/or with further algorithmoptimization using the method of the invention. In these cases, themethod of the invention correctly classified the samples as unaffectedfor trisomy 21, 18, or 13 and as male or female.

In this study, 38/532 analyzed samples were from women who underwentassisted reproduction. Of these, 17/38 samples had chromosomalabnormalities; no false positives or false negatives were detected inthis sub-population.

TABLE 27 Sensitivity and Specificity of the Method Sensi- Speci-Performance tivity (%) 95% CI ficity (%) 95% CI Trisomy 21 100.0  95.9-100.0 100.0 99.1-100.0 (n = 493) (89/89) (404/404) Trisomy 18 97.285.5-99.9 100   99.2-100.0 (n = 496) (35/36) (460/460) Trisomy 13 78.649.2-99.9 100.0 99.2-100.0 (n = 499) (11/14) (485/485) Female 99.6 97.6->99.9  99.5 97.2->99.9 (n = 433) (232/233) (199/200) Male 100.0  98.0-100.0 100.0 98.5-100.0 (n = 433) (184/184) (249/249) Monosomy X93.8 69.8-99.8  99.8 98.7->99.9 (n = 433) (15/16) (416/417)

Discussion

This prospective study to determine whole chromosome fetal aneuploidyfrom maternal plasma was designed to emulate the real world scenario ofsample collection, processing and analysis. Whole blood samples wereobtained at the enrollment sites, did not require immediate processing,and were shipped overnight to the sequencing laboratory. In contrast toa prior prospective study that only involved chromosome 21 (Palomaki etal., Genetics in Medicine 2011:1), in this study, all eligible sampleswith any abnormal karyotype were sequenced and analyzed. The sequencinglaboratory did not have prior knowledge of which fetal chromosomes mightbe affected nor the ratio of aneuploid to euploid samples. The studydesign recruited a high-risk study population of pregnant women toassure a statistically significant prevalence of aneuploidy, and Tables25 and 26 indicate the complexity of the karyotypes that were analyzed.The results demonstrate that: i) fetal aneuploidies (including thoseresulting from translocation trisomy, mosaicism, and complex variations)can be detected with high sensitivity and specificity and ii) aneuploidyin one chromosome does not affect the ability of the method of theinvention to correctly identify the euploid status of other chromosomes.The algorithms utilized in the previous studies appear to be unable toeffectively determine other aneuploidies that inevitably would bepresent in a general clinical population (Erich et al., Am J ObstetGynecol 2011 March; 204(3):205 e1-11, Chiu et al., BMJ 2011;342:c7401).

With regard to mosaicism, the analysis of sequencing information in thisstudy was able to correctly classify samples that had mosaic karyotypesfor chromosomes 21 and 18 in 4/4 affected samples. These resultsdemonstrate the sensitivity of the analysis for detecting specificcharacteristics of cell free DNA in a complex mixture. In one case, thesequencing data for chromosome 2 indicated a whole or partial chromosomeaneuploidy while the amniocentesis karyotype result for chromosome 2 wasdiploid. In two other examples, one sample with 47,XXX karyotype andanother with a 46,XX karyotype, the method of the invention classifiedthese samples as monosomy X. It is possible these are mosaic cases, orthat the pregnant woman herself is mosaic. (It is important to rememberthat the sequencing is performed on total DNA, which is a combination ofmaternal and fetal DNA.) While cytogenetic analysis of amniocytes orvilli from invasive procedures is currently the reference standard foraneuploidy classification, a karyotype performed on a limited number ofcells cannot rule out low-level mosaicism. The current clinical studydesign did not include long term infant follow-up or access to placentaltissue at delivery, so we are unable to determine if these were true orfalse positive results. We speculate that the specificity of thesequencing process, coupled with optimized algorithms according to themethod of the invention to detect genome wide variation, may ultimatelyprovide more sensitive identification of fetal DNA abnormalities,particularly in cases of mosaicism, than standard karyotyping.

The International Society for Prenatal Diagnosis has issued a RapidResponse Statement commenting on the commercial availability ofmassively parallel sequencing (MPS) for prenatal detection of Downsyndrome (Benn et al., Prenat Diagn 2012 doi:10.1002/pd.2919). Theystate that before routine MPS-based population screening for fetal Downsyndrome is introduced, evidence is needed that the test performs insome sub-populations, such as in women who conceive by in vitrofertilization. The results reported here suggest that the present methodis accurate in this group of pregnant women, many of whom are at highrisk for aneuploidy.

Although these results demonstrate the excellent performance of thepresent method with optimized algorithms for aneuploidy detection acrossthe genome in singleton pregnancies from women at increased risk foraneuploidy, more experience, particularly in low-risk populations, isneeded to build confidence in the diagnostic performance of the methodwhen the prevalence is low and in multiple gestation. In the earlystages of clinical implementation, classification of chromosomes 21, 18and 13 using sequencing information according to the present methodshould be utilized after a positive first or second trimester screeningresult. This will reduce unnecessary invasive procedures caused by thefalse positive screening results, with a concomitant reduction inprocedure related adverse events. Invasive procedures could be limitedto confirmation of a positive result from sequencing. However, thatthere are clinical scenarios (e.g., advanced maternal age andinfertility) in which pregnant women will want to avoid an invasiveprocedure; they may request this test as an alternative to the primaryscreen and/or invasive procedure. All patients should receive thoroughpre-test counseling to ensure that they understand the limitations ofthe test and the implications of the results. As experience accumulateswith more samples, it is possible that this test will replace currentscreening protocols and become a primary screening and ultimately anoninvasive diagnostic test for fetal aneuploidy.

Example 17

Determining Fetal Fraction from NCV to Distinguish the Presence ofComplete or Partial Fetal Chromosomal Aneuploidies in Analytical Samples

Given that the chromosome dose for a fetal chromosome of interest in amaternal sample increases proportionately with increasing fetalfraction, it is expected that a ff value that is based on the NCV valuefor a complete chromosome of interest would be determinative of thepresence or absence of a complete fetal chromosomal aneuploidy. Todemonstrate that ff determined from NCVs can be used to distinguish thepresence of a complete chromosomal aneuploidy from a partial chromosomalaneuploidy or the contribution from a mosaic sample, genomic DNA frommothers and from their children were used to create artificial samplesthat simulated the mixture of fetal and maternal cfDNA found in thecirculation of a pregnant woman. The NCV based value of fetal fractionis a form of putative fetal fraction described above.

The DNA of the mothers and children was purchased from Coriell Institutefor Medical Research (Camden, N.J.). DNA identification and samplekaryotype are given in Table 27.

TABLE 27 Example 17 Artificial Clinical mixture # Condition Coriell IDFamily Member Comments Pres Karyotype 1 Whole NG09387 2139 Mother NormalNormal 46, XX trisomy; NG09394 2139 Son Affected T21 Downs Syndrome 47,XY, +21 2 Deletion NA10924 1313 Mother Normal Normal 46, XX NA10925 1313Son Deletion in 7 Grieg 46, XY, Encephaly del(7)(pter>p14::p12>qter) 3Mosaic NA22629 2877 Mother Deletion in 11 Affected 46, XX, del(11)NA22628 2877 Son Deletion in 11, Affected 47, XY, Mos dup 15 mosaicdel(11)(pter−>p12::p11.2−>qter), +15[12]/46, XY,del(11)(pter−>p12::p11.2−>qter)[40].arr 11p12p11.12(41392049-49104319)x14 Duplication NA16368 1925 Mother Normal 46, XX.arr(1-22, X)x2 NA163631925 Nor Twin Monozygotic Normal 46, XY Son Twins, NA16362 1925 Affectedtwin one normal, Affected 47, XY, +der(22) son; partial T22 one affected

Samples comprising complete chromosomal or partial chromosomalaneuploidies were analyzed as follows.

In all cases, genomic DNA from the mother and genomic DNA from the childwere sheared by sonication with a peak at 200 bp. Artificial samplescomprising mothers' DNA with 0%, 5% or 10% w/w of the child's DNA spikedin were processed to prepare sequencing libraries, which were sequencedin a massively parallel fashion using sequencing-by-synthesis asdescribed in Example 12. Each artificial DNA sample was sequenced fourtimes using separate flow cells on the sequencer to provide 4 sets ofsequence information for each of samples containing 0%, 5% and 10% childDNA. 36 bp reads were aligned to human reference genome hg19, anduniquely mapped tags were counted. Approximately 125×10⁶ sequence tagswere obtained for each of the 4 flow cell lanes used per sample.

Normalizing chromosomes (single or group of chromosomes) were identifiedin a set of qualified samples comprising 20 male and 20 female gDNAlibraries, as described elsewhere herein. Normalizing chromosomes forchromosome 21 were identified chr4+chr16+chr22 normalizing chromosomesfor chromosome 7 were identified aschr4+chr6+chr8+chr12+chr19+chr20normalizing chromosomes for chromosome15 were identified as chr9+chr12+chr14+chr19+chr20, normalizingchromosome for chromosome 22 were identified as chr19 and normalizingchromosomes for chromosome X were identified as chr4+chr6+chr7+chr8.Sequence tags for the chromosome of interest and for the correspondingnormalizing chromosome (single chromosome or group of chromosomes)obtained from sequencing the artificial samples were counted and used tocalculate chromosome doses, and calculate NCVs.

In the instant example, the ff determined using NCV for chromosome 21 ina sample mixture (1) where NCV_(2IA) is the NCV value determined forchromosome 21 in the test sample (1), which comprises the triploidchromosome 21, and CV_(21U) is the coefficient of variation for doses ofchromosome 21 determined in the qualified samples (comprising diploidchromosome 21); and where NCV_(XA) is the NCV value determined forchromosome X in the test sample (1), which comprises the triploidchromosome 21, and CV_(XU) is the coefficient of variation for doses ofchromosome X determined in the qualified samples (comprising diploidchromosome 21).

FIG. 56 shows a plot of the percent “ff” determined using doses ofchromosome 21 (ff₂₁) as a function of the percent “ff” determined usingdoses of chromosome X (ff_(X)) in a synthetic maternal sample (1)comprising DNA from a child with trisomy 21.

The data shows that the chromosome doses and the NCVs derived therefromincrease in proportion with increasing ff, and that there is a 1:1relationship between the percent ff determined using doses for thetriploid chromosome i.e. chromosome 21, and the percent ff determinedusing doses for a chromosome known to be present as a single chromosomei.e. chromosome X.

FIG. 57 shows a plot of the percent “ff” determined using doses ofchromosome 7 (ff₇) as a function of the percent “ff” determined usingdoses of chromosome X (ff_(X)) in a synthetic maternal sample (2)comprising DNA from a euploid mother and her child who carries a partialdeletion in chromosome 7.

As was shown for samples (1) and (2), the data show that the chromosomedoses and the NCVs derived therefrom increase in proportion withincreasing ff. However, in a case where the aneuploidy is a partialchromosomal aneuploidy, the percent ff determined using chromosome dosesof a partially aneuploid chromosome (ff₇) does not correspond to thepercent ff determined using doses for chromosome X (ff_(X)). Therefore,deviation from the 1:1 relationship shown for a complete trisomic sampleis indicative of the presence of a partial aneuploidy.

FIG. 58 shows a plot of the percent “ff” determined using doses ofchromosome 15 (ff₁₅) as a function of the percent “ff” determined usingdoses of chromosome X (ff_(X)) in a synthetic maternal sample (3)comprising DNA from a euploid mother and her child who is 25% mosaicwith a partial duplication of chromosome 15.

As was shown for samples (1) and (2), the ff determined using doses andthe NCVs derived therefrom increase in proportion with increasing ff. Aswas shown in sample (2), sample (3) comprises a partial chromosomalaneuploidy, and the percent ff determined using chromosome doses of apartially aneuploid chromosome (ff₁₅) does not correspond to the percentff determined using doses for chromosome X (ff_(X)). The lack ofcorrespondence between the two ff is indicative of the presence of apartial aneuploidy rather than a complete chromosomal aneuploidy.

FIG. 59 shows a plot of the percent “ff” determined using doses ofchromosome 22 (ff₂₂) and the NCVs derived therefrom in artificial sample(4) comprising 0% child DNA (i), and 10% DNA from an unaffected twin sonknown not to have a partial chromosomal aneuploidy of chromosome 22(ii), and 10% DNA from the affected twin son known to have a partialchromosomal aneuploidy of chromosome 22 (iii). The data show that the“ff” for the sample comprising the DNA from the unaffected twin anddetermined from the four NCVs calculated from doses of chromosome 22 areclose to zero, indicating the absence of an aneuploidy of chromosome 22in the unaffected child; and the “ff” of the unaffected twin whencalculated from doses of chromosome X confirm that the “ff” for theunaffected twin sample is about 10%. The data also show that the “ff”for the sample comprising DNA from the affected twin and determined fromthe four NCVs calculated from doses of chromosome 22 (ff₂₂) is about 3%,indicating the presence of an aneuploidy in chromosome 22; while the“ff” when calculated from doses of chromosome X (ff_(X)) confirm thatthe “ff” for the unaffected twin sample is about 10%. The lack ofcorrespondence between the ff₂₂ and ff_(X) indicates that the aneuploidyof chromosome 22 in the affected twin is a partial chromosomalaneuploidy.

Therefore, the data shows that in maternal samples comprising cfDNA of amale fetus, the chromosome doses and the NCV values derived therefromcan be used to distinguish the presence of a complete trisomy from apartial aneuploidy and/or a complete or partial aneuploidy present in amosaic sample. The partial aneuploidy can be an increase or a decreaseof part of a chromosome. Optionally, resolution of the partialaneuploidy and/or mosaicism can be obtained by using chromosome dosesand Estimated Fetal Fraction values as described in Example 12.

The fetal fraction methods described above can also be utilized todetermine the likelihood that one or more of the fetus' inmulti-gestational pregnancy has an aneuploidy. For example, in one caseof fraternal twins the fetal fraction determined from the NCV_(X) valuewas found to be 8.3% while that measured from the NCV₂₁ value was 5.0%.This suggested that only one of the pair of male fetuses had a T21aneuploidy, and this result is confirmed by the karyotype result. Inanother example with maternal twins the fetal fraction determined fromthe X chromosome was 7.3% whereas fetal fraction determined fromchromosome 18 was 8.9%. In this example, both twins were determined tobe T18 males from karyotype.

Example 18

Determining Fetal Fraction from NCV to Identify the Presence of CompleteFetal Chromosomal Aneuploidies in Clinical Samples

To demonstrate that a ff determined from NCVs (CNff) can be used todistinguish the presence of a complete chromosomal aneuploidy from apartial chromosomal aneuploidy in a clinical sample, chromosomes ofinterest 21, 13, and 18 were quantified in clinical samples using cfDNAobtained from the blood of pregnant women. The presence of trisomy wasverified by karyotpe.

cfDNA was obtained from 46 maternal samples from pregnant women eachcarrying a male fetus with trisomy 21 (T21), 13 maternal samples frompregnant women each carrying a fetus with trisomy 18 (T18), and 3maternal samples from pregnant women carrying a male fetus with trisomy13 (T13). These clinical samples were samples from the clinical studydescribed in Example 16. cfDNA was isolated, and sequencing librarieswere prepared as described in Example 16, but using the new Illumina v3chemistry.

Sequencing libraries made from cfDNA from qualified samples known to beunaffected for chromosomes 21, 18 and 13 were also sequenced using theIllumin v3 chemistry. Sequence reads obtained for the qualified sampleswere mapped to human reference genome hg19 and Sequence reads thatuniquely mapped all chromosome sequences corresponding to humanreference genome hg19 (non-repeat masked) were counted and used tosystematically determine which chromosome or group of chromosomes wouldserve as the normalizing chromosome for each of chromosomes of interest21, 18, and 13 in the test samples.

Table 28 below shows the normalizing chromosomes (denominatorchromosomes) identified to be used to determine chromosome doses(ratios) for chromosomes 1-22, X and Y in each of the test samples.

TABLE 28 Example 18 - Normalizing chromosomes systematically identifiedfor use in T21, T18, and T13 test samples chromosome % cv_1 mean_1stdv_1 denominator_1 chr1 0.17328043 0.40761174 0.00070631 chr2 +chr10 + chr15 + chr20 + chr22 chr2 0.12704695 0.28019322 0.00035598chr1 + chr4 + chr6 + chr8 + chr10 chr3 0.15988408 0.40355832 0.00064523chr5 + chr6 + chr8 chr4 1.74801104 1.01640701 0.01776691 chr5 chr50.12567875 0.26828505 0.00033718 chr3 + chr4 + chr8 + chr12 chr60.18609738 0.23679013 0.00044066 chr2 + chr3 + chr4 + chr14 chr70.15420267 0.14975583 0.00023093 chr4 + chr5 + chr6 + chr8 + chr9 +chr12 + chr19 + chr22 chr8 0.16386037 0.14886515 0.00024393 chr3 +chr4 + chr5 + chr11 + chr12 + chr14 + chr20 chr9 0.14260705 0.078662010.00011218 chr1 + chr2 + chr5 + chr7 + chr8 + chr11 + chr14 + chr15 +chr16 + chr17 + chr22 chr10 0.23668533 0.27352768 0.0006474 chr1 +chr6 + chr20 + chr22 chr11 0.15337497 0.18482929 0.00028348 chr1 +chr5 + chr8 + chr16 + chr20 + chr22 chr12 0.15469865 0.169938620.00026289 chr3 + chr5 + chr6 + chr14 + chr17 + chr20 chr13 0.438183680.26647091 0.00116763 chr4 + chr6 chr14 0.21119571 0.25952538 0.00054811chr5 + chr12 + chr22 chr15 0.43655328 0.19120781 0.00083472 chr1 +chr10 + chr20 chr16 0.40796729 0.2909714 0.00118707 chr15 + chr17 +chr19 + chr20 chr17 0.43044876 0.42765351 0.00184083 chr16 + chr20 +chr22 chr18 0.2411015 0.23996728 0.00057856 chr5 + chr8 chr19 1.315246831.42233899 0.01870727 chr22 chr20 0.32975718 0.17240557 0.00056852chr10 + chr16 + chr17 + chr19 + chr22 chr21 0.43611264 0.085161480.0003714 chr4 + chr14 + chr16 + chr17 chr22 1.31897082 0.703188390.00927485 chr19 chrx 0.67161441 0.28361966 0.00190483 chr4 + chr5 +chr8 chry 12.85179682 0.00035758 0.00004596 chr4 + chr7

Having identified the normalizing chromosomes in the qualified samples,the test samples were sequenced, and sequence tags mapping to each ofchromosomes 21, 18, 13, and corresponding normalizing chromosomes in thetest samples were counted and used to calculate chromosome doses(ratios). NCV values were then calculated as described previouslyaccording to

$\begin{matrix}{{NCV}_{jA} = \frac{R_{jA} - \overset{\_}{R_{jU}}}{\sigma_{jU}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$

For each of the test samples, the fetal fraction was determined forchromosome x and for the chromosome of interest according to theequation

ff _((i))=2*NCV _(jA) CV _(jU)  Equation 25

described elsewhere in the specification.

FIG. 60 shows a plot of the CNffx versus CNff21 determined in thesamples comprising the fetal T21 trisomy. As expected for a completechromosomal aneuploidy, the CNffx matched that determined using NCVsfrom chromosome 21 (CNff21).

Similarly, CNffx matched that determined using NCVs from chromosome 18(CNff18) in the T18 test samples (FIG. 61), and CNffx matched thatdetermined using NCVs from chromosome13 (CNff13) in the T13 test samples(FIG. 62).

FIG. 60 also shows the fetal fraction obtained for the samples withfemale fetuses affected by T21. As expected, CNff21 in these “female”samples could not be verified by comparison to chromosome X. In order toverify the CNff21 for the female samples, CNff can be determined for achromosome known not to fbe aneuploid in a fetus e.g. chromosome 1.Alternatively, CNff21 for “female” samples can be confirmed by comparingit to a NCNff e.g. one determined by counting tags to polymorphicsequences, as described elsewhere herein.

Therefore, the number of sequence tags and the derived NCV values thatidentify copy number variations of complete chromosomes can be used todetermine the corresponding fetal fraction in the aneuploid/affectedsamples. Correspondence in the CNff for a chromosome of interest withthat of a chromosome known not to be aneuploid can be used to confirmthe presence of a complete chromosomal trisomy.

Example 19

Determining Fetal Fraction from NCV to Identify the Presence of PartialFetal Chromosomal Aneuploidies in Clinical Samples

To demonstrate that a ff determined from NCVs (CNff) can be used toidentify and localize the presence of a partial chromosomal aneuploidyfrom a partial chromosomal aneuploidy in a clinical sample, cfDNA from aclinical that had been identified as having an aneuploidy in chromosome17, was sequenced and analyzed as described in Example 18.

Using sequence tags mapped to chromosome 17 in the test sample, and tonormalizing chromosomes chr16+chr20+chr22 that been identified in theset of qualified samples (Table 28 above), NCV values for each ofchromosomes in the test sample were calculated.

FIG. 63 shows a plot of NCV values for chromosomes 1-22 and X in thetest sample. As is shown in the plot, the NCV value for chromosome 17was determined to have an NCV>4, which is the threshold that had beenchosen for identifying aneuploid chromosomes. The plot also shows theNCV value for chromosome X, which as expected had a negative NCV.

The CNff for chromosome 17 and chromosome X were calculated according to

ff _((i))=2*NCV _(jA) CV _(jU)  Equation 25

and determined to be CNff17=3.9% and CNffX=13.5%.

The discrepancy between the CNff indicated the presence of either apartial aneuploidy or possibly of a mosaicism.

To distinguish the partial aneuploidy from a possible mosaicism, thenumber of tags counted for each of 100 Kbp consecutive blocks/bins onchromosome 17, and a normalized bin value (NBV) was calculated for eachbin. Normalization of the number of tags in individual bins wasperformed by determining the ratio of tags/bin to the sum of the numberof tags in 20 bins of identical size and having a GC content closest tothat of the bin being analyzed. Thus, in this instance, normalizationwas related to GC content. Optionally, bin normalization can also berelated to the variability in bin dose as determined in qualifiedsamples as described for chromosome doses/ratios. In this example, theGCC Z-score is equivalent to the NBV value determined as

$\begin{matrix}{{NBV}_{ij} = \frac{x_{ij} - {Mj}}{MAD}} & {{Equation}\mspace{14mu} 26}\end{matrix}$

where M_(j) and MAD_(j) are the estimated median and median adjusteddeviation, respectively, for the j-th chromosome dose in a set ofqualified samples, and x_(ij) is the observed j-th chromosome dose fortest sample i.

The normalized bin values (NBV) for each of the 100 Kbp bins along thelength of chromosome 17 are shown on the Y-axis of FIG. 64 as GCCZ-score, indicating the GC normalization. The plot shown in FIG. 64clearly shows an increase in copy number of the bins corresponding toapproximately the last 200,000 bp of chromosome 17. This finding was inagreement with the karyotype provided for the sample indicating aduplication at the q ter of chromosome 17.

Therefore, CNff can be used to identify and to localize partialaneuploidies in chromosomes.

What is claimed is:
 1. A method, implemented using a computer systemcomprising one or more processors and system memory, of identifying atleast one normalizing sequence for normalizing coverage of a sequence ofinterest suspected of having a copy number variation in an organism, themethod comprising: (a) obtaining, by the computer system, sequence readsgenerated by sequencing a plurality of qualified samples comprisingnucleic acids for the sequence of interest, wherein the qualifiedsamples are known to have a normal copy number of the sequence ofinterest; (b) aligning, by the one or more processors, the sequencereads to a reference genome including the sequence of interest andthereby providing sequence tags corresponding to the sequence reads; (c)identifying, by the one or more processors, a number of those sequencetags that are from the sequence of interest and identifying a number ofthose sequence tags that are from a plurality of potential normalizingsequences; (d) repeatedly determining, by the one or more processors,sequence doses for the sequence of interest using the number of sequencetags for the sequence of interest and the number of sequence tags forthe plurality of potential normalizing sequences; and (e) selecting, bythe one or more processors, the at least one normalizing sequence, aloneor in a combination with one or more other potential normalizingsequences, giving sequence doses for the sequence of interest having:(i) the smallest variability among two or more of the qualified samples,(ii) the greatest differentiability between two or more of the qualifiedsamples and one or more affected samples known to have a copy numbervariation of the sequence of interest, (iii) the smallest variabilityand the greatest differentiability, or (iv) a combination of smallvariability and large differentiability.
 2. The method of claim 1,wherein selecting the normalizing sequence comprises selecting acombination of normalizing sequences that gives the smallest variabilityin doses across the plurality of qualified samples.
 3. The method ofclaim 1, wherein selecting the normalizing sequence comprises selectingthe normalizing sequence alone, and not in combination with othernormalizing sequences, that gives the smallest variability in dosesacross the plurality of qualified samples.
 4. The method of claim 1,wherein selecting the normalizing sequence comprises selecting acombination of normalizing sequences that gives the greatestdifferentiability in calculated doses between an aneuploid test sampleand the plurality of qualified samples.
 5. The method of claim 1,wherein selecting the normalizing sequence comprises selecting thenormalizing sequence alone, and not in combination with othernormalizing sequences, that gives the greatest differentiability incalculated doses between an aneuploid test sample and the plurality ofqualified samples.
 6. The method of claim 1, wherein thedifferentiability comprises a statistical measure of a differencebetween a distribution of the sequence doses of the two or morequalified samples and a distribution of the sequence doses of the one ormore affected samples.
 7. The method of claim 1, further comprisingsequencing nucleic acid in the qualified samples using massivelyparallel sequencing.
 8. The method of claim 1, wherein the sequence ofinterest comprises a chromosome.
 9. The method of claim 1, wherein thesequence of interest comprises a chromosome segment associated with apartial aneuploidy.
 10. The method of claim 1, wherein the sequence ofinterest comprises a sequence related to a cancer.
 11. The method ofclaim 1, wherein applying the normalizing sequence in a copy numbervariation analysis of the sequence of interest improves sensitivity,selectivity, and/or reliability of the copy number variation analysis.12. The method of claim 1, wherein the qualified samples comprise DNAfrom two genomes.
 13. The method of claim 1, wherein the qualifiedsamples comprise cell-free DNA.
 14. The method of claim 1, wherein thequalified samples are obtained from mothers pregnant with a fetus thathas been confirmed to have a normal copy number of the sequence ofinterest.
 15. The method of claim 1, wherein a qualified samplecomprises a maternal plasma sample that contains a mixture of fetal andmaternal cfDNA molecules.
 16. The method of claim 1, wherein thequalified samples comprise at least 50 qualified samples.
 17. The methodof claim 1, wherein a sequence dose comprises a ratio of a readabundance measurement of the sequence of interest over the readabundance measurement of the normalizing sequence.
 18. The method ofclaim 1, further comprising: providing sequence read coverage of thesequence of interest in the organism; normalizing sequence read coverageof the sequence of interest using the normalizing sequence; anddetermining a copy number variation of the sequence of interest in theorganism using the normalized sequence read coverage of the sequence ofinterest.
 19. A computer program product comprising a non-transitorycomputer readable medium storing program code that, when executed by oneor more processors of a computer system, causes the computer system todetermine a normalizing sequence of a sequence of interest, said programcode comprising: (a) code for obtaining sequence reads generated using anucleic acid sequencer from a plurality of qualified samples for thesequence of interest, wherein the qualified samples are known to have anormal copy number of the sequence of interest; (b) code for aligningthe sequence reads to a reference genome including the sequence ofinterest and thereby providing sequence tags corresponding to thesequence reads; (c) code for identifying a number of those sequence tagsthat are from the sequence of interest and identifying a number of thosesequence tags that are from a plurality of potential normalizingsequences; (d) code for repeatedly determining sequence doses for thesequence of interest using the number of sequence tags for the sequenceof interest and the number of sequence tags for the plurality ofpotential normalizing sequences; and (e) code for selecting thenormalizing sequence, alone or in a combination with one or more otherpotential normalizing sequences, giving sequence doses for the sequenceof interest having: (i) the smallest variability among two or more ofthe qualified samples, (ii) the greatest differentiability between twoor more of the qualified samples and one or more affected samples knownto have a copy number variation of the sequence of interest, (iii) thesmallest variability and the greatest differentiability, or (iv) acombination of small variability and large differentiability.
 20. Asystem comprising one or more processors, system memory, and one or morecomputer-readable storage media having stored thereon instructions forexecution on said processor to: (a) receive sequence reads obtainedusing a nucleic acid sequencer from a plurality of qualified samples fora sequence of interest, wherein the qualified samples are known to havea normal copy number of the sequence of interest; (b) align the sequencereads to a reference genome including the sequence of interest andthereby providing sequence tags corresponding to the sequence reads; (c)identify a number of those sequence tags that are from the sequence ofinterest and identifying a number of those sequence tags that are from aplurality of potential normalizing sequences; (d) repeatedly determinesequence doses for the sequence of interest using the number of sequencetags for the sequence of interest and the number of sequence tags forthe plurality of potential normalizing sequences; and (e) select anormalizing sequence, alone or in a combination with one or more otherpotential normalizing sequences, giving sequence doses for the sequenceof interest having: (i) the smallest variability among two or more ofthe qualified samples, (ii) the greatest differentiability between twoor more of the qualified samples and one or more affected samples knownto have a copy number variation of the sequence of interest, (iii) thesmallest variability and the greatest differentiability, or (iv) acombination of small variability and large differentiability.